Biomarkers for pancreatic cancer and diagnostic methods

ABSTRACT

Methods and related kits for differentiating pancreatic cancer from a benign pancreatic disease. The method includes assaying a patient biological sample for a total level of CA 19-9 antigen and for a glycan level in specific mucin(s), and comparing the total level of CA 19-9 antigen and the glycan level in the specific mucin(s) to statistically validated thresholds, wherein a different level of total CA 19-9 antigen in the patient biological sample as compared to a statistically validated threshold and a different level of glycan level in the specific mucin(s) as compared to statistically validated thresholds indicate pancreatic cancer in the patient rather than a benign pancreatic disease.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. applicationSer. No. 11/375,756 filed Mar. 15, 2006, now U.S. Pat. No. 7,838,634,entitled METHODS FOR MEASURING GLYCAN LEVELS OF PROTEINS, which claimsthe benefit of provisional application Ser. No. 60/671,829 filed Apr.15, 2005, entitled DCP BIOMARKER FOR PANCREATIC CANCER, the entirecontents of which applications are incorporated herein in theirentirety.

This invention was made with government support under R33 CA122890 andR21 CA112153 awarded by the National institute of Health. The governmenthas certain rights in the invention.

FIELD OF THE INVENTION

This invention relates to the field of molecular biology and medicineand specifically to biomarkers, kits, and methods for diagnosingpancreatic cancer.

BACKGROUND OF THE INVENTION

Methods to detect cancers more accurately or at earlier stages couldlead to better outcomes for many cancer patients, including patents withpancreatic cancer. The success of this goal is facilitated bytechnologies that allow the rapid profiling and characterization ofcandidate biomarkers. Many technologies for that purpose are indevelopment, each with unique optimal applications and advantages anddisadvantages. Affinity-based methods, using antibodies or otheraffinity reagents, are preferred in applications where reproducible,specific and relatively high-throughput protein detection is required.The value of affinity-based methods has been enhanced through the use ofmicroarrays, which allow multiplexed and high-throughput proteinanalysis in low sample volumes.

Pancreatic cancer is typically diagnosed at a late stage. The late stagedetection combined with few treatment options lead to five year survivalrates of less than 5%. Yeo, C. J., Cameron, J. L., Lillemoe, K. D.,Sitzmann, J. V., Hruban, R. H., Goodman, S. N., Dooley, W. C., Coleman,J., and Pitt, H. A. Pancreaticoduodenectomy for cancer of the head ofthe pancreas. 201 patients. Ann Surg, 221: 721-731; discussion 731-723,1995.

Because established disease can be difficult to diagnose due to clinicalsimilarities with certain benign diseases such as chronic pancreatitis[2], some patients may receive sub-optimal treatment. Current diagnosticmodalities include non-invasive imaging, endoscopic ultrasound, andcytology based on fine-needle aspiration [3]. These methods are usefulfor identifying pancreatic abnormalities and rendering an accuratediagnosis in many cases, but they come with high cost, significantexpertise required for interpretation, and inherent uncertainty.

Blood-based diagnostic tests for pancreatic would be especially valuablebecause of the potential for routine and inexpensive screening. Severalserum markers previously have been investigated for pancreatic cancerdiagnostics. The CA 19-9 antigen—a carbohydrate blood group antigen—iselevated in 50-75% of pancreatic cancer cases and is typically used toconfirm diagnosis or to monitor a patient's progress after surgery.Riker, A., Libutti, S. K., and Bartlett, D. L. Advances in the earlydetection, diagnosis, and staging of pancreatic cancer. SurgicalOncology, 6: 157-169, 1998. CA 19-9 is not used for early screeningsince it is not present in patients with certain blood types and isoften elevated in benign disease.

Other carbohydrate antigens are associated with pancreatic cancer, suchas SPAN-1 (Frena, A. SPan-1 and exocrine pancreatic carcinoma. Theclinical role of a new tumor marker. Int J Biol Markers, 16: 189-197,2001); DUPAN-2 (Kawa, S., Oguchi, H., Kobayashi, T., Tokoo, M., Furuta,S., Kanai, M., and Homma, T. Elevated serum levels of Dupan-2 inpancreatic cancer patients negative for Lewis blood group phenotype. BrJ Cancer, 64: 899-902, 1991); CEA; CA-50; 90K (Gentiloni, N., Caradonna,P., Costamagna, B., E'Ostilio, N., Perri, V., Mutignani, M., Febbraro,S., Tinari, N., Iacobelli, S., and Natoli, C. Pancreatic juice 90K andserum CA 19-9 combined determination can discriminate between pancreaticcancer and chronic pancreatitis. Amer. J. Gastroenterology, 90:1069-1072, 1995); CA 195 (Hyoty, M., Hyoty, H., Aaran, R. K., Airo, I.,and Nordback, I. Tumour antigens CA 195 and CA 19-9 in pancreatic juiceand serum for the diagnosis of pancreatic carcinoma. Eur J Surg, 158:173-179, 1992); TUM2-PK (Oremek, G. M., Eigenbrodt, E., Radle, J.,Zeuzem, S., and Seiffert, U. B. Value of the serum levels of the tumormarker TUM2-PK in pancreatic cancer. Anticancer Res, 17: 3031-3033,1997); and CA 242 (Pasanen, P. A., Eskelinen, M., Partanen, K.,Pikkarainen, P., Penttila, I., and Alhava, E. Multivariate analysis ofsix serum tumor markers (CEA, CA 50, CA 242, TPA, TPS, TATI) andconventional laboratory tests in the diagnosis of hepatopancreatobiliarymalignancy. Anticancer Res, 15: 2731-2737, 1995).

Certain changes that occur in the sera of pancreatic cancer patientsreflect the high level of inflammation associated with the disease.Pro-inflammatory cytokines, such as IL-6 and IL-8 (Wigmore, S. J.,Fearon, K. C., Sangster, K., Maingay, J. P., Garden, O. J., and Ross, J.A. Cytokine regulation of constitutive production of interleukin-8 and-6 by human pancreatic cancer cell lines and serum cytokineconcentrations in patients with pancreatic cancer. Int J Oncol, 21:881-886, 2002), and the acute phase reactant C-reactive protein (CRP)are usually elevated in the sera of pancreatic cancer patients. Fearon,K. C., Barber, M. D., Falconer, J. S., McMillan, D. C., Ross, J. A., andPreston, T. Pancreatic cancer as a model: inflammatory mediators,acute-phase response, and cancer cachexia. World J Surg, 23: 584-588,1999. Numerous other proteins have been evaluated as serum biomarkersfor pancreatic cancer. The performance of tests based on single markersso far has not been good enough to be recommended for clinicalapplication.

Prior studies were performed measuring one protein at a time, usingsample and reagent volumes that in most cases prohibited large-scalestudies of multiple candidate markers. The measurement of many putativecancer-associated serum proteins together, as enabled by antibodymicroarrays, has valuable uses. Multiple candidate markers areefficiently screened, allowing a broad characterization of the types ofalterations present in cancer sera, and multiple measurements can beused in combination to potentially improve the diagnostic accuracy.Multiple, independent markers may be grouped together to improvediagnostic performance if the markers contribute complementary,non-overlapping discrimination information. The challenge for pancreaticcancer diagnostics is to find the particular protein alterations orcombinations of protein alterations that usually occur early in cancerdevelopment and that do not occur in benign conditions.

The application of antibody and protein microarray methods to cancerresearch has been demonstrated in studies on proteins in sera, cellculture, and resected tissue samples. Miller, J. C., Zhou, H., Kwekel,J., Cavallo, R., Burke, J., Butler, E. B., Teh, B. S., and Haab, B. B.Antibody microarray profiling of human prostate cancer sera: antibodyscreening and identification of potential biomarkers. Proteomics, 3:56-63, 2003; Huang, R.-P., Huang, R., Fan, Y., and Lin, Y. Simultaneousdetection of multiple cytokines from conditioned media and patient'ssera by an antibody-based protein array system. Anal. Biochem., 294:55-62, 2001; Huang, R., Lin, Y., Shi, Q., Flowers, L., Ramachandran, S.,Horowitz, I. R., Parthasarathy, S., and Huang, R. P. Enhanced proteinprofiling arrays with ELISA-based amplification for high-throughputmolecular changes of tumor patients' plasma. Clin Cancer Res, 10:598-609, 2004; Zhou, H., Bouwman, K., Schotanus, M., Verweij, C.,Marrero, J. A., Dillon, D., Costa, J., Lizardi, P. M., and Haab, B. B.Two-color, rolling-circle amplification on antibody microarrays forsensitive, multiplexed serum-protein measurements. Genome Biology; 5:R28, 2004; Hamelinck, D., Zhou, H., Li, L., Verweij, C., Dillon, D.,Feng, Z., Costa, J., and Haab, B. B. Optimized normalization forantibody microarrays and application to serum-protein profiling. MolCell Proteomics, 2005; Sreekumar, A., Nyati, M. K., Varambally, S.,Barrette, T. R., Ghosh, D., Lawrence, T. S., and Chinnaiyan, A. M.Profiling of cancer cells using protein microarrays: discovery of novelradiation-regulated proteins. Cancer Research, 61: 7585-7593, 2001; Lin,Y., Huang, R., Cao, X., Wang, S. M., Shi, Q., and Huang, R. P. Detectionof multiple cytokines by protein arrays from cell lysate and tissuelysate. Clin Chem Lab Med, 41: 139-145, 2003; Knezevic, V., Leethanakul,C., Bichsel, V. E., Worth, J. M., Prabhu, V. V., Gutkind, J. S., Liotta,L. A., Munson, P. J., Petricoin, E. F. I., and Krizman, D. B. Proteomicprofiling of the cancer microenvironment by antibody arrays. Proteomics,1: 1271-1278, 2001; Tannapfel, A., Anhalt, K., Hausermann, P., Sommerer,F., Benicke, M., Uhlmann, D., Witzigmann, H., Nauss, J., and Wittekind,C. Identification of novel proteins associated with hepatocellularcarcinomas using protein microarrays. J Pathol, 201: 238-249, 2003;Hudelist, G., Pacher-Zavisin, M., Singer, C. F., Holper, T., Kubista,E., Schreiber, M., Manavi, M., Bilban, M., and Czerwenka, K. Use ofhigh-throughput protein array for profiling of differentially expressedproteins in normal and malignant breast tissue. Breast Cancer Res Treat,86: 281-291, 2004.

Alterations to Post-Translationally-Modified Proteins

Many proteins are modified through glycosylation, or the attachment ofcarbohydrate chains at specific locations. The structures of thesechains are precisely regulated and often play a major role in proteinfunction. Glycosylation is an important determinant of protein function,and changes in glycosylation are thought to play roles in certaindisease processes, including cancer. Thus, the ability to efficientlyprofile and measure variations in glycosylation on multiple proteins andin multiple samples is valuable to identify disease-associated glycansalterations and new diagnostic markers. Specifically, the ability toefficiently profile the variation in glycosylation could lead to theidentification of disease-associated glycan alterations and newdiagnostic biomarkers.

The current methods for analyzing glycans are either cumbersome or verylow throughput and not reproducible enough for diagnostics research.Traditionally, glycan structure is studied by enzymatic or chemicalcleavage of carbohydrate groups, followed by gel or chromatographyanalysis and perhaps mass spectrometry analysis. While these methods areuseful for determining glycan structures, they are not suitable forstudies requiring reproducible measurements over many different samplesor proteins, or for determining variation between populations ofsamples.

Affinity chromatography methods have been used to measure abundances ofglycans. Useful affinity reagents for carbohydrate research arelectins—plant and animal proteins with natural carbohydrate bindingfunctionality. Lectins have been used in a variety of formats such asaffinity chromatography to isolate glycoproteins and modified ELISAs.Lectins and antibodies against carbohydrate epitopes have been used toidentify cancer-associated glycosylation, although those methods do notidentify which proteins are carrying the epitopes. Affinitychromatography methods could be coupled to immunoprecipitation methodsto measure glycans on specific proteins.

As discussed below, the inventor has developed a high throughputaffinity-based method that is practical for multiplexed studies. Theinventor has applied the method of the present invention to the study ofchanges in glycan levels on serum proteins in pancreatic cancerpatients. Further, the inventor has identified various biomarkers that,alone or in combination, are useful in methods of diagnosing pancreaticcancer including methods of differentiating pancreatic cancer from otherpancreatic diseases.

SUMMARY OF THE INVENTION

The CA 19-9 assay detects a carbohydrate antigen on multiple proteincarriers, some of which may be preferential carriers of the antigen incancer. The inventors examined whether measurement of the CA 19-9antigen on individual proteins could improve performance over thestandard “total CA 19-9 assay”. They used antibody arrays to measure thelevels of the CA 19-9 antigen on multiple proteins in serum or plasmasamples from patients with pancreatic adenocarcinoma or pancreatitis.Sample sets from three different institutions were examined, comprising420 individual samples. A subset of cancer patients with no elevation inthe standard CA 19-9 assay showed elevations of the CA 19-9 antigenspecifically on the proteins MUC1, MUC5AC, or MUC16 in all three samplesets. By combining measurements of total CA 19-9 with CA 19-9 on theseindividual proteins, the sensitivity of cancer detection was raised to85-100% in the three sample sets, at a specificity of 75%.

The present invention includes a method for differentiating pancreaticcancer from a benign pancreatic disease, including obtaining a patientbiological sample from a patient having or suspected of having apancreatic disease; assaying the patient biological sample (a) for atotal level of CA 19-9 antigen in the patient biological sample and (b)for a glycan level in a specific mucin(s) in the patient biologicalsample; comparing the total level of CA 19-9 antigen in the patientbiological sample to a statistically validated threshold for total CA19-9 antigen, which statistically validated threshold for total CA 19-9antigen is based on a total level of CA 19-9 antigen in comparablecontrol biological samples from patients having a pancreatic diseaseother than pancreatic cancer; and comparing the glycan level in thespecific mucin(s) in the patient biological sample to a statisticallyvalidated threshold for the specific mucin(s), which statisticallyvalidated threshold for the specific mucin(s) is based on a glycan levelin the specific mucin(s) in comparable control biological samples frompatients having a pancreatic disease other than pancreatic cancer;wherein (a) a different level of total CA 19-9 antigen in the patientbiological sample as compared to the statistically validated thresholdfor total CA 19-9 antigen and (b) a different level of glycan level inthe specific mucin(s) in the patient biological sample as compared tothe statistically validated threshold for the specific mucin(s)indicates pancreatic cancer in the patient rather than a benignpancreatic disease.

In various embodiments of the present methods, the mucin(s) may be oneor more of MUC1, MUC5AC, and MUC16 (or any combination thereof); thepatient biological sample may be plasma or serum; and pancreatitis maybe the benign pancreatic disease. In other embodiments, the method alsomay include diagnosing pancreatic cancer in the patient; reporting theindication of pancreatic cancer to the patient or a physician; providinga treatment for pancreatic cancer to the patient; providing a monoclonalantibody to the CA-19-9 antigen and using the monoclonal antibody inassaying for the total CA 19-9 antigen in the patient biological sampleand the glycan level in the specific mucin(s) in the patient biologicalsample; and/or the mucin(s) may be assayed with a glycan binding proteinother than the monoclonal antibody to the CA 19-9 antigen.

The present invention also includes a kit for differentiating pancreaticcancer from a benign pancreatic disease (such as pancreatitis) includingan antibody array having (a) a CA 19-9 capture antibody bound theretoand (b) one or more specific mucin capture antibodies bound thereto,which specific mucin capture antibodies are selected from the groupconsisting of an anti-MUC1 antibody, an anti-MUC5AC antibody, and ananti-MUC16 antibody; a detection monoclonal antibody to the CA-19-9antigen; and a container for the detection monoclonal antibody to theCA-19-9 antigen.

The present kit also may include a glycan binding protein other than thedetection monoclonal antibody to the CA 19-9 antigen, and a containerfor the glycan binding protein other than the detection monoclonalantibody to the CA 19-9 antigen; and the glycan binding protein otherthan the detection monoclonal antibody to the CA 19-9 antigen may beAleuria Aurantia lectin (AAL), Wheat Germ Agglutinin (WGA), Jacalin,Bauhinea Purpurea lectin (BPL), Sambucus Nigra lectin (SNA), or aglycan-binding antibody.

BRIEF DESCRIPTION OF THE DRAWING

FIGS. 1A-D are scanned images of microarrays.

FIGS. 2A-C are histograms showing antibody performance and comparison ofsurface types.

FIG. 3 shows a Western blot analysis characterizing selected antibodies.

FIG. 4A-H are graphs showing antibody binding validation using analytedilutions.

FIG. 5A-C shows distributions of measurements for antibodiescontributing to the classifications.

FIGS. 6 and 7 are graphs showing the results of an antibody microarrayin healthy patients and patients with pancreatic cancer measured both byDCP level and level of glycosylation of DCP.

FIGS. 8 and 9A-C are cluster image maps for all the antibodies and forthe antibodies that are different between the patient classes.

FIGS. 10A-B are schematic drawings depicting two methods of measuringglycan groups on specific proteins.

FIGS. 11A-C show detection of glycans on antibody arrays. FIG. 11A showsa schematic drawing of one-color glycan detection. FIG. 11B shows aschematic drawing of two-color detection of glycans and proteins, usingdigoxigenin-labeled proteins. FIG. 11C shows scanned images of antibodyarrays incubated with no serum, unlabeled serum, or digoxigenin-labeledserum.

FIGS. 12A-D show blocking non-specific GBP binding to captureantibodies. FIG. 12A shows the chemical structures used to modifyglycans on the spotted antibodies. FIG. 12B is a schematic drawing ofblocking lectin binding to spotted antibodies. FIG. 12C is scannedimages of antibody arrays, as follows: biotinylated AAL was incubatedand detected on antibody arrays that were unblocked and incubated withPBS buffer (top left), unblocked and incubated with serum (top right),blocked and incubated with PBS buffer (bottom left), and blocked andincubated with serum (bottom right). FIG. 12D shows the ratios of AALbinding with serum incubation to without serum incubation after blocking(dark squares) and without blocking (open circles) are shown for eachantibody.

FIGS. 13A-B are graphs showing specificity of lectin binding to capturedglycans. FIG. 13A shows data for the lectin WGA pre-incubated withvarying molar ratios of either N-,N-,diacetyl chitobiose (circles) orsucrose (squares), and FIG. 13B shows equivalent data from the lectinAAL pre-incubated with varying molar ratios of L-fucose (open circles)or sucrose (squares).

FIGS. 14A-B show distributions of protein and glycan levels andgel-based validation. FIG. 14A shows the results of twenty-three controlsamples (white bars) and 23 cancer samples (dark bars) labeled withdigoxigenin, incubated on antibody arrays, and detected withbiotinylated AAL followed by streptavidin-phycoerythrin and Cy5-labeledanti-digoxigenin. FIG. 14B shows a Western blot analysis of seven toeight samples each from the cancer (lanes marked with C) and healthy(lanes marked with H) patients that were separated by gelelectrophoresis, blotted, and probed with an antibody againsthaptoglobin (Hp), an antibody against alpha-2-macroglobulin (a2Mb), orAAL, as indicated.

FIGS. 15A-C show comparison of protein and glycan levels using parallelsandwich and glycan-detection assays. FIG. 15A shows representativeimages of arrays using various (indicated) samples and detectionantibodies. FIG. 15B is a graph showing the distribution of the levelsof control samples (white bars) and carrier samples (dark bars). FIG.15C is a scatter plot comparison of the levels detected at each captureantibody by either the anti-protein antibodies (y-axis) or theanti-CA19-9 antibody (x-axis) for each control patient serum sample(dark triangle) and each cancer patient serum sample (open circles).

FIG. 16 shows clusters of glycan measurements using, only antibodiesthat discriminated the classes.

FIG. 17 shows clusters of glycan measurement using all antibodies.

FIG. 18 shows images of antibody microarrays chemically blocked, andincubated with either a buffer solution (top arrays) or blood serum(bottom arrays).

FIGS. 19A and 19B show detection of total CA19-9 and CA 19-9 onindividual proteins using antibody arrays. FIG. 19A showshigh-throughput sample processing and array-based sandwich assays forCA19-9 detection. Forty-eight identical arrays are printed on onemicroscopic slide, segregated by hydrophobic wax boundaries (left). Aset of serum or plasma samples are incubated on the arrays in randomorder, and the arrays for the entire sample set are probed with the CA19-9 detection antibody. Total CA 19-9 is measured at the CA19-9 captureantibody, and CA19-9 on specific proteins is measured at the individualantibodies against those proteins (right). FIG. 19B shows representativeraw image data from each of the sample groups. Triplicates of eachantibody were randomly positioned on the array.

FIG. 20 shows distribution of total CA19-9 levels in pancreatic cancerand pancreatitis patients from all three sample sets. Each pointrepresents an individual sample. The boxes indicate the quartiles, withthe median indicated by the solid horizontal lines, and the verticallines mark the ranges. The blue dashed lines indicate the thresholdselected for further analysis at 75% specificity.

FIG. 21 shows raw images of arrays from subgroups defined by totalCA19-9. Cancer samples that were detected by CA 19-9 (true positive),not detected by CA 19-9 but picked up by the panel, or not detected byCA 19-9 or the panel are represented. In addition, pancreatitis samplesthat were not detected by CA 19-9 (true negative) or detected by CA 19-9(false positive) are represented. The sample identifier is given withineach array. In the subgroup picked up by the panel (top-right), theantibody used to detect a given sample is listed adjacent to each array.The corresponding antibody spots are underlined in white. Two arrays forsample LC3607 are shown, one detected with BPL (rightmost column, row2), and the other detected with CA19-9 (rightmost column, row 3). Allother arrays were detected with CA19-9. The bottom panels show maps ofantibodies targeting MUC16 (left), MUC5AC (middle), and MUC1 (right).

FIGS. 22A-C show subgroups of cancer patients defined by CA 19-9 carrierproteins. The samples were divided by CA 19-9 status and clusteredseparately. The clusters include patients with total CA 19-9 level inthe top quintile (FIG. 22A); middle quintile (FIG. 22B); and bottomquintile (FIG. 22C). Each box represents a measurement from an antibody(indicated by the row labels) in a sample (indicated by the columnlabels). For clarity, the fluorescence values were converted toquintiles over the entire set, as indicated by the color scale. Thecolumn labels of the cancer samples are highlighted gray. The color barsabove each cluster denote samples that show the CA 19-9 antigen on atleast some of the mucins (red bars) or on none of the mucins (greenbars). The # symbol indicates pancreatitis samples above the 75%specificity threshold, and * indicates cancer samples below thethreshold.

FIGS. 23A-C show markers complementary to total CA 19-9. FIG. 23A showsa comparison of CA19-9 on MUC16 to total CA19-9. The levels of CA 19-9on MUC for each sample are plotted along the vertical axis, and thetotal CA 19-9 levels for the same samples are plotted along thehorizontal axis. The plot shows only the lower 50% of the samples bytotal CA 19-9. The vertical line indicates the threshold defined to give75% specificity by total CA 19-9. The horizontal dashed line indicates aproposed threshold for CA19-9 on MUC16 which would result in thedetection of additional cancer samples (noted by the arrows) withoutdetecting additional pancreatitis samples. FIG. 23B shows the combinedresults of total CA 19-9 and four additional complementary markers. Thesamples are ordered in the columns (Bn is benign, EarlyC is early-stagecancer, LateC is late-stage cancer, Cancer is unknown stage cancer) andthe markers in the rows. The threshold for total CA19-9 was set to 75%specificity, and the threshold for each additional marker was defined asin panel a. A yellow square indicates a measurement above the threshold,a black square indicates below the threshold, and gray squares aremissing data. The blue box denotes the cancer samples not detected by CA19-9 (CA 19-9 measurements in the red box). The samples picked up by theadditional markers are highlighted by blue column labels. FIG. 23C showscomparisons of panel performance in duplicate sets. The signalintensities of each marker were median-centered within each dataset toprovide a common baseline between the two datasets, and a threshold wasdetermined for each marker in each set using the strategy describedabove. The thresholds were applied to the opposite set, and theresulting level of discrimination was assessed. The marker that detectedeach sample (indicated in the rows) is given for each application of themarker panels.

FIG. 24A-C show panel performance in additional sample sets. FIG. 24Ashows a comparison of CA 19-9 on MUC16 and total CA 19-9 in Sets 1 and2. Specificity was fixed at approximately 75% by total CA19-9, and thethreshold for CA 19-9 on MUC16 was defined as in FIG. 21A. FIGS. 24B-Cshow biomarker panels in Sets 1 and 2. The yellow squares indicatemeasurements above the threshold for a given marker, black indicatesbelow the threshold, and gray indicates missing data. Each columnrepresents an individual sample, and the row indicates the marker usedwith antibody ID followed in parenthesis. The blue boxes highlight thesample(s) picked up by the panel, and the red and white boxes indicatethe false negatives defined by total CA19-9 and the panel, respectively.FIG. 24B shows Set 1, comprising late-stage (left) and early-stage(right) cancer patients. FIG. 24C shows Set 2, comprising a mix of earlyand late stage patients. In both sets, the samples from the pancreatitispatients are not shown.

FIG. 25 shows CA 19-9 immunoblots of selected samples. Of fundamentalinterest is the distribution of CA 19-9 carrier proteins in thesesubgroups. An approach to visualize the range of proteins carrying theCA 19-9 antigen is to fractionate the plasma proteins using SDS-PAGE andimmunoblot for the CA 19-9 antigen, which we did for representativesamples from the subgroups defined by CA 19-9 carrier protein status.The indicated plasma samples from Set #1 were fractionated on a 4-12%gradient polyacrylamide gel and probed by Western blot using the CA 19-9antibody. The samples that were high in CA 19-9 by microarray showed abroad range of molecular weights with high signal, indicating manyproteins containing the CA 19-9 antigen. The samples that were below the75% specificity threshold but that showed significant signal at themucin proteins showed only faint bands at high molecular weights (>150kD); and the samples not detected by any marker showed no discernable oronly faint bands. This results shows that no major protein carriers ofthe CA 19-9 antigen, at least in the molecular weights observed in thisformat, are present in the low CA 19-9 samples. Thus, the identificationof cancer in the remaining samples not picked up by the panel mostlikely will rely on additional proteins or glycans.

FIG. 26 shows total CA19-9 and CA 19-9 on each protein captured on thearray. The samples are ordered in the columns and the markers in therows. The threshold for total CA19-9 was set to 75% specificity, and thethreshold for each additional marker was defined as in panel a. Only thecancer samples and the benign samples showing positive CA 19-9 valuesare shown. A yellow square indicates a measurement above the threshold,a black square indicates below the threshold, and gray squares aremissing data. The blue box denotes the cancer samples not detected by CA19-9 (the total CA 19-9 label is highlighted red). The markers used inthe panel have bolded row labels. FP, false positive; TP, true positive;FN, false negative.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Before the subject invention is described further, it is to beunderstood that the invention is not limited to the particularembodiments of the invention described below, as variations of theparticular embodiments may be made and still fall within the scope ofthe appended claims. It is also to be understood that the terminologyemployed is for the purpose of describing particular embodiments, and isnot intended to be limiting. Instead, the scope of the present inventionwill be established by the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range, and any other stated or intervening value in thatstated range, is encompassed within the invention. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges, and are also encompassed within the invention, subjectto any specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the invention.

All references, patents, patent publications, articles, and databases,referred to in this application are incorporated herein by reference intheir entirety, as if each were specifically and individuallyincorporated herein by reference. Such patents, patent publications,articles, and databases are incorporated for the purpose of describingand disclosing the subject components of the invention that aredescribed in those patents, patent publications, articles, anddatabases, which components might be used in connection with thepresently described invention. The information provided below is notadmitted to be prior art to the present invention, but is providedsolely to assist the understanding of the reader.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,embodiments, and advantages of the invention will be apparent from thedescription and drawings, and from the claims. The preferred embodimentsof the present invention may be understood more readily by reference tothe following detailed description of the specific embodiments and theExamples included hereafter.

For clarity of disclosure, and not by way of limitation, the detaileddescription of the invention is divided into the subsections thatfollow.

Unless defined otherwise, all technical and scientific terms used hereinhave the meaning commonly understood by one of ordinary skill in the artto which this invention belongs. Generally, the nomenclature used hereinand the laboratory procedures in cell culture, molecular genetics,organic chemistry and nucleic acid and protein chemistry described beloware those well known and commonly employed in the art. Although anymethods, devices and materials similar or equivalent to those describedherein can be used in the practice or testing of the invention, thepreferred methods, devices and materials are now described.

The preferred embodiments of the present invention may be understoodmore readily by reference to the following detailed description ofpreferred embodiments included hereafter.

DEFINITIONS

In this specification and the appended claims, the singular forms “a,”“an” and “the” include plural reference unless the context clearlydictates otherwise.

As used in the present application, “biological sample” means any fluidor other material derived from the body of a normal or diseased subject,such as blood, serum, plasma, lymph, urine, saliva, tears, cerebrospinalfluid, milk, amniotic fluid, bile, ascites fluid, pus, and the like.Also included within the meaning of the term “biological sample” is anorgan or tissue extract and culture fluid in which any cells or tissuepreparation from a subject has been incubated.

The term “pancreatic cancer” means a malignant neoplasm of the pancreascharacterized by the abnormal proliferation of cells, the growth ofwhich cells exceeds and is uncoordinated with that of the normal tissuesaround it.

The term “subject” or “patient” as used herein refers to a mammal,preferably a human, in need of diagnosis and/or treatment for acondition, disorder or disease.

The term “treatment” or “treating” as used herein refers to theadministration of medicine or the performance of medical procedures withrespect to a subject, for either prophylaxis (prevention) or to cure orreduce the extent of or likelihood of occurrence or recurrence of theinfirmity or malady or condition or event in the instance where thesubject or patient is afflicted. As related to the present invention,the term may also mean the administration of medicine or the performanceof medical procedures as therapy, prevention or prophylaxis ofpancreatic cancer.

The present invention utilizes antibody microarray technology andapplies it to the discovery of serum biomarkers for pancreatic cancer.Underlying this invention are the components involved in the applicationof antibody microarrays to biomarker research and the strategy used toprofile serum protein abundances in serum samples from pancreatic cancerpatients, patients with benign pancreatic disease, and healthy controlsubjects. The present invention probes the variation in multiple typesof proteins in the sera and the use of the multiple measurements incombination for sample classification. More specifically, the presentinvention utilizes antibody microarrays to probe for, and determine therelative concentration of target proteins in a tissue sample ofpancreatic cancer.

Microarrays are orderly arrangements of spatially resolved samples orprobes (in the present invention, antibodies of known specificity to aparticular protein).

The underlying concept of antibody microarray depends on binding betweenproteins and antibodies specific to proteins. Microarray technology addsautomation to the process of resolving proteins of particular identitypresent in an analyte sample by labeling, preferably with fluorescentlabels and subsequent binding to a specific antibody immobilized to asolid support in microarray format. An experiment with a single antibodymicroarray chip can provide simultaneous information on protein levelsof many genes. Antibody microarray experiments employ common solidsupports such as glass slides, upon which antibodies are deposited atspecific locations (addresses).

Antibody microarray analysis generally involves injecting afluorescently tagged sample of proteins into a chamber on a microarrayslide to bind with antibodies having specific affinity for thoseproteins (and subsequent laser excitation at the interface of the arraysurface and the tagged sample; collection of fluorescence emissions by alens; optical filtration of the fluorescence emissions; fluorescencedetection; and quantification of intensity).

Antibodies used in connection with the present invention arecommercially available or may be synthesized by standard methods knownin the art.

High concentrations of certain protein products are indicative ofpancreatic cancer. Such proteins are targets for early diagnostic assaysof pancreatic cancer. The proteins can be detected by some assay means,e.g., immunoassay, in some accessible body fluid or tissue. For example,enzyme-linked immunosorbent assays (ELISAs) can be used to detect targetprotein concentrations or levels. ELISAs rely on antibodies coupled toan easily-assayed enzyme. ELISA can be used to detect the presence ofproteins that are recognized by an antibody. A basic ELISA assay is amultiple-step procedure: 1) applying a sample or antigen to themicroliter plate wells; 2) blocking all unbound sites to prevent falsepositive results; 3) adding antibody to the wells; 4) adding anti-mouseIgG conjugated to an enzyme; 5) reacting a substrate with the enzyme toproduce a colored product, thus indicating a positive reaction. Thereare many variations of basic ELISAs.

The present invention contemplates assay methods and a diagnostic kitfor pancreatic cancer which distinguishes a pancreatic tumor from benignconditions. For example, total CA 19-9 levels can be combined withglycan levels on one or more of MUC1, MUC5AC, and MUC16 to distinguishpancreatic cancer from other diseases of the pancreas (e.g.,pancreatitis).

Diagnostic targets are especially useful if they can be detected in abiological sample before the cancer presents as a tumor, preferably, aprotein or peptide ligand or, more preferably, an antibody is used todetect presence and levels of the target protein. A a protein or peptideligand also may be used to detect glycosylation levels of a targetprotein.

Suitable detectable labels include radioactive, fluorescent,fluorogenic, chromogenic, or other chemical labels. Useful radio labels,which are detected by gamma counter, scintillation counter, or autoradiography include ³H, ¹²⁵I, ¹³¹I, ³⁵S, and ¹⁴C.

Common fluorescent labels include fluorescein, rhodamine, dansyl,phycoerythrin, phycocyanin, allophycocyanin, o phthaldehyde, andfluoroescamine. The fluorophoor, such as the dansyl group, must beexcited by light of a particular wavelength to fluoresce. The proteincan also be labeled for detection using fluorescence-emitting metalssuch as ¹⁵²Eu, or others of the lanthanide series.

An application of the present invention is screening of high-risksubjects—those with a family history of pancreatic cancer, or patientswith other risk factors such as chronic pancreatitis, obesity, heavysmoking, and possibly diabetes. The prevalence of the disease, andtherefore the positive predictive value of the test, would be higher insuch a population. There are at least two possible tests: one todistinguish pancreatic disease from no pancreatic disease, and if thattest is positive, another to distinguish malignant from benign disease.A positive result in both tests would dictate evaluation by a computedtomography (CT) scan or a more invasive modality such as endoscopicultrasound (EUS) or endoscopic retrograde cholangiopancreatography(ERCP).

Further, the use and detection of various combinations of the biomarkerscan provide more accurate and definitive detection of pancreatic cancer.The measurement of the biomarkers in the blood serum or plasma could beused as a means to detect or more accurately diagnose or stagepancreatic cancer. Such biomarkers either can be proteins or glycans onspecific proteins.

More specifically, as described in greater detail below, the inventorused antibody microarrays to probe the associations of multiple serumproteins with pancreatic cancer and to explore the use of combinedmeasurements for sample classification. Serum samples from pancreaticcancer patients (n=61), patients with benign pancreatic disease (n=31),and healthy control subjects (n=50) were probed in replicate experimentsets by two-color, rolling-circle amplification on microarrayscontaining 92 antibodies and control proteins. The antibodies that hadreproducibly different binding levels between the patient classesrevealed different types of alterations, reflecting inflammation (highC-reactive protein, alpha-1-antitrypsin and serum amyloid A), immuneresponse (high IgA), leakage of cell breakdown products (low plasmagelsolin), and possibly altered glycosylation and glucose regulation(high PIVKA-II). The accuracy of the most significant antibodymicroarray measurements was confirmed through immunoblot and antigendilution experiments. Sample classification algorithms were formed usingthree different multiparametric methods. A logistic regression withforward selection method distinguished the cancer samples from thehealthy control samples with a 90% and 93% sensitivity and a 90% and 94%specificity in duplicate experiment sets. The cancer samples weredistinguished from the benign disease samples with a 95% and 92%sensitivity and a 88% and 74% specificity in duplicate experiment sets.The classification accuracies using each multiparametric method weresignificantly improved over those achieved using individual antibodies.Thus, this invention provides insights into the nature of theserum-protein alterations in pancreatic cancer patients and furthervalidated the potential for improved sample classification accuracyusing multiplexed measurements.

Four sets of antibody microarray profiling were performed on serumsamples from pancreatic cancer patients and controls. In the first twoexperiment sets (sets one & two), 105 samples were used from fourclasses: healthy subjects (n=33), pancreatic adenocarcinoma patients(n=32), patients with benign pancreatic diseases (n=18), and patientswith other gastro-intestinal malignancies (n=22). In the second twoexperiment sets (sets three & four), 142 samples were used from threeclasses: healthy subjects (n=50), pancreatic adenocarcinoma patients(n=61), and patients with benign pancreatic diseases (n=31). In eachexperiment set, a Two-Color, Rolling-Circle Amplification (TC-RCA) assaywas used. The biotin-labeled proteins of each serum sample were mixedwith digoxigenin-labeled proteins of a reference pool, made up of eitherall the samples or half the samples pooled together. Thesample-reference mixes were incubated on antibody microarrays containing88-90 antibodies and controls, and the relative binding ofbiotin-labeled proteins to digoxigenin-labeled proteins was detected ateach antibody spot using TC-RCA. Table 1 presents the antibodies used inthe studies along with a summary of performance characteristics.

TABLE 1 Experiments 1 & 2 Control Experiments Corr. Sample S/B S/B NameCompany Cat. # Used Avg. CV Correlation Overlap 543 543anti-α-fetoprotein (ab #1) Biotrend 4F16 2, 3, 4 43.25 1.65anti-α-fetoprotein (ab #2) Sigma A8452 1, 2, 3, 4 0.16 0.73 89 26.994.76 anti-α-1 antichymotrypsin Fitzgerald 10-A08 1, 2, 3, 4 0.19 0.67 8820.04 0.09 anti-α-2 antiplasmin Cedarlanes CL20005AP 1, 2, 3, 4 0.180.83 89 45.69 0.28 anti-Albumin Fitzgerald 10-A75 1, 2, 3, 4 0.16 0.6988 27.26 0.28 anti-Alkaline phosphatase Biotrend 0300-0430 1, 2, 3, 40.12 0.83 89 22.61 0.13 anti-Alpha 2-macroglobulin Sigma M1893 1, 2, 3,4 0.13 0.89 89 98.66 0.58 anti-Alpha-1-antitrypsin (ab #1) Biotrend0640-5507 1, 2, 3, 4 0.14 0.80 87 24.83 0.20 anti-Alpha-1-antitrypsin(ab #2) GenWay A10066F 1, 2, 3, 4 0.23 0.58 89 17.53 4.97anti-Angiostatin Oncogene GF47 1, 2 0.33 0.66 14 22.22 0.06 Researchanti-β2-Microglobulin US Biological M3890-05X 1, 2, 3, 4 0.21 0.66 8943.43 2.14 anti-CA125(ovarian cancer US Biological C0050-01 1, 2, 3, 40.15 0.50 64 7.70 0.38 antigen) anti-CA15-3(breast cancer antigen USBiological C0050-23 1, 2, 3, 4 0.11 0.82 78 9.66 0.08 MUC1)anti-CA19-9(cancer antigen Sialyl US Biological C0075-07 1, 2, 3, 4 0.160.66 89 20.05 0.12 Lewis A ) (ab #1) anti-CA19-9(cancer antigen SialylUS Biological C0075-27 4 Lewis A) (ab #2) anti-Catalase AbCam ab1877 1,2, 3, 4 0.14 0.93 89 117.99 0.37 anti-Cathepsin D R&D Systems AF1014 1,2, 3, 4 0.16 0.95 89 36.62 0.10 anti-Caveolin-1 Sigma C3237 1, 2, 3, 40.14 0.91 89 27.14 0.10 anti-CD26(dipeptidyl peptidase US BiologicalC2277-10X 1, 2, 3, 4 0.14 0.65 89 29.10 0.25 IV) anti-CEA(carcinoembryonic US Biological C1299-94 1, 2, 3, 4 0.14 0.75 89 25.500.11 antigen) anti-Ceruloplasmin Sigma C0911 1, 2, 3, 4 0.23 0.89 8943.84 0.10 anti-Chorioric gonadotropin AbCam ab9502 1, 2, 3, 4 0.14 0.8389 14.97 0.11 anti-CKBB (creatin kinase BB is US Biological C7910-16 1,2, 3, 4 39.40 28.54 oenzyme) anti-Complement C3 Sigma C7761 1, 2, 3, 40.12 0.82 89 76.29 0.28 anti-Complement C5 US Biological C7850-24 1, 2,3, 4 0.12 0.83 89 107.40 0.61 anti-CRP (C reactive protein) (ab SigmaC1688 1, 2, 3, 4 0.16 0.82 89 47.51 0.17 #1) anti-CRP (C reactiveprotein) (ab US Biological C7907-10 1, 2, 3, 4 0.22 0.96 71 38.86 0.43#2) anti-Ferritin US Biological F4015-17 1, 2, 3, 4 0.22 0.90 88 50.870.46 anti-Gelsolin Sigma G4896 1, 2, 3, 4 0.17 0.85 89 38.73 0.10anti-GST (glutathione S US Biological G8135-05 1, 2, 3, 4 0.19 0.93 8932.95 0.09 transferase) anti-Haptoglobin Biotrend 4890-0004 1, 2, 3, 40.12 0.92 82 20.94 0.11 anti-HC II (heparin cofactor II) CedarlanesCL20070AP 1, 2, 3, 4 0.27 0.78 89 63.52 0.14 anti-Hemoglobin BethylE80-135(kit 1, 2, 3, 4 0.18 0.90 88 70.48 0.75 component) anti-HGF(hepatocyte growth R&D Systems MAB294 1, 2, 3, 4 0.14 0.78 87 16.78 0.09factor) anti-IgA Bethyl E80-102(kit 1, 2, 3, 4 0.14 0.85 88 109.74 1.88component) anti-IG FBP-3(insulin-like growth R&D Systems MAB305 1, 2, 3,4 0.15 0.81 72 9.05 0.08 factor binding protein) anti-IGF-1(insulin-like growth R&D Systems AF-291-NA 1, 2, 3, 4 0.17 0.92 8728.05 0.13 factor) anti-IgG1 Zymed 05-3300 1, 2, 3, 4 0.11 0.89 89 69.420.30 Laboratories anti-IgG-Fc Bethyl E80-104(kit 1, 2, 3, 4 0.12 0.82 8996.94 1.60 component) anti-IgM Jackson 109-005-043 1, 2, 3, 4 0.20 0.7589 46.90 0.16 Immunoresearch anti-IL-1α (ab #1) Research RDI-IL1AabrP 1,2, 3, 4 0.23 0.63 62 8.90 0.03 Diagnostics anti-IL-1α (ab #2) Gen WayA22493 1, 2, 3, 4 0.16 0.94 88 31.01 0.34 anti-IL-1β Research RDI-IL1BabrP 1, 2, 3, 4 0 0.91 0.03 Diagnostics anti-IL-2 R&D Systems MAB202 1,2, 3, 4 0.13 0.83 87 15.06 0.07 anti-IL-2Rα R&D Systems MAB223 1, 2, 3,4 0.14 0.73 59 6.79 0.06 anti-IL-6 (ab #1) Sigma 17901 1, 2, 3, 4 0.320.63 86 28.45 0.07 anti-IL-6 (ab #2) R&D Systems MAB206 1, 2, 3, 4 0.150.74 73 9.54 0.08 anti-IL-6 sR R&D Systems AF-227-NA 1, 2, 3, 4 0.340.36 18 4.89 0.03 anti-IL-8 (ab #1) R&D Systems AB-208-NA 1, 2, 3, 40.17 0.93 87 27.40 0.20 anti-IL-8 (ab #2) GenWay A21030F 1, 2, 3, 4 0.170.91 86 22.10 0.12 anti-IL-10 (ab #1) R&D Systems MAB217 1, 2, 3, 4 0.190.70 83 11.80 0.05 anti-IL-10 (ab #2) GenWay A21031F 1, 2, 3, 4 0.150.94 89 29.65 0.43 anti-KRT18 (keratin, type I GenWay A22753 1, 2, 3, 40.19 0.92 89 32.35 0.25 cytoskelatal 18) anti-Laminin (ab #1) Sigma L8271 1, 2, 3, 4 0.22 0.92 89 43.26 0.75 anti-Laminin (ab #2) GenWayA20044F 1, 2, 3, 4 0.15 0.95 89 30.86 0.09 anti-Lipase US BiologicalL2496-02 1, 2, 3, 4 0.14 0.77 87 11.47 0.12 anti-M2-PK-(pyruvate kinasetype Scehbo S-1 1, 2, 3, 4 1.02 1 5.24 0.08 M2) anti-MCP-1 (monocyteGenex GEA6043-2 1, 2, 3, 4 0.16 0.89 89 105.34 0.91 chemoatractantprotein) Biosciences Inc. anti-MPM2(Ser/Thr, Pro US Biological M4685 1,2, 3, 4 0.19 0.61 88 15.95 0.05 phosphorylated mitotic protein)anti-Ornithine decarb oxylase Neomarkers MS-464- 1, 2, 3, 4 0.19 0.75 7913.08 0.29 PIABX anti-PAI-1 (plasminogen activator Cedarlanes CL20140A1, 2, 3, 4 0.18 0.89 89 57.96 0.38 inhibitor type-1) anti-PIVKA-II(protein induced Crystal Chem 08040 1, 2, 3, 4 0.18 0.60 89 23.37 0.20vitamin K antagonist) Inc. anti-Plasminogen US Biological P4256-27A 1,2, 3, 4 0.17 0.93 88 41.94 0.11 anti-Serum amyloid A AbCam ab687 1, 2,3, 4 0 5.69 0.02 anti-sTNFR (soluble tumor US Biological T9162 1, 2, 3,4 0.24 0.59 58 6.11 0.03 necrosis factor receptor 1) anti-ThioredoxinMedical & M063-3 1, 2, 3, 4 0.26 0.49 80 11.28 0.03 BiologicalLaboratories anti-TIMP-1 (tissue inhibitor of BIOMOL SA-373 1, 2, 3, 40.25 0.61 69 9.26 0.06 matrix metalloproteinases-1) anti-TNFα OncogeneGF31 1, 2, 3, 4 0.33 0.67 32 8.24 0.12 Research anti-Transferrin BethylE80-128(kit 3, 4 component) anti-Troponin I Chemicon Intl. MAB1691 1, 2,3, 4 0.15 0.81 89 22.04 0.17 Inc. anti-TSP-1 (thrombospondin-1)Neomarkers MS-419- 1, 2, 3, 4 0.11 0.85 81 1298 0.12 PIABX Experiments 3& 4 Sample Control Avg. Corr. S/B S/B Name CV Correlation Overlap 543543 anti-α-fetoprotein (ab #1) 0.11 0.95 138 29.12 0.41anti-α-fetoprotein (ab #2) 0.16 0.79 138 50.10 17.36 anti-α-1antichymotrypsin 66.71 79.68 anti-α-2 antiplasmin 0.14 0.88 138 52.280.12 anti-Albumin 0.18 0.52 138 52.75 3.87 anti-Alkaline phosphatase0.13 0.95 136 51.97 1.00 anti-Alpha 2-macroglobulin 0.11 0.90 138 69.720.27 anti-Alpha-1-antitrypsin (ab #1) 0.14 0.83 137 58.01 1.30anti-Alpha-1-antitrypsin (ab #2) 0.15 0.84 138 42.91 0.80anti-Angiostatin anti-β2-Microglobulin 54.45 85.30 anti-CA125(ovariancancer 0.15 0.75 138 24.85 0.11 antigen) anti-CA15-3(breast cancerantigen 0.11 0.90 138 21.33 0.04 MUC1) anti-CA19-9(cancer antigen Sialyl0.12 0.87 135 27.88 0.60 Lewis A ) (ab #1) anti-CA19-9(cancer antigenSialyl 37.31 0.59 Lewis A) (ab #2) anti-Catalase 0.12 0.92 138 55.660.31 anti-Cathepsin D 0.11 0.98 137 43.03 0.23 anti-Caveolin-1 0.17 0.89138 37.35 1.13 anti-CD26(dipeptidyl peptidase 0.16 0.81 138 45.85 3.97IV) anti-CEA (carcinoembryonic 0.11 0.89 138 30.13 0.63 antigen)anti-Ceruloplasmin 0.11 0.97 134 25.82 0.02 anti-Chorioric gonadotropin0.16 0.87 130 22.03 1.36 anti-CKBB (creatin kinase BB is 0.19 0.90 13624.56 0.73 oenzyme) anti-Complement C3 0.10 0.88 138 49.51 0.42anti-Complement C5 0.12 0.89 138 53.71 0.19 anti-CRP (C reactiveprotein) (ab 0.13 0.87 138 36.05 0.29 #1) anti-CRP (C reactive protein)(ab 0.18 0.98 107 45.50 0.50 #2) anti-Ferritin 0.14 0.97 131 37.88 3.61anti-Gelsolin 0.15 0.82 124 18.25 0.05 anti-GST (glutathione S 0.18 0.91136 40.35 0.12 transferase) anti-Haptoglobin 0.17 0.86 137 38.68 1.45anti-HC II (heparin cofactor II) 0.21 0.84 138 52.31 0.10anti-Hemoglobin 0.12 0.94 137 51.81 0.12 anti-HGF (hepatocyte growth0.13 0.86 138 43.72 1.82 factor) anti-IgA 0.15 0.82 136 90.69 0.31anti-IG FBP-3(insulin-like growth 0.10 0.92 134 26.31 0.44 factorbinding protein) anti-IG F-1(insulin-like growth 0.32 0.89 136 35.610.16 factor) anti-IgG1 0.11 0.91 138 85.58 0.37 anti-IgG-Fc 0.14 0.81138 116.30 1.64 anti-IgM 0.15 0.90 138 90.48 22.38 anti-IL-1α (ab #1)0.16 0.88 137 26.64 0.19 anti-IL-1α (ab #2) 0.21 0.91 138 31.22 0.14anti-IL-1β 0.21 0.77 92 8.56 0.02 anti-IL-2 0.10 0.92 138 26.50 0.18anti-IL-2Rα 0.10 0.92 128 24.25 0.41 anti-IL-6 (ab #1) 0.15 0.72 9216.76 0.29 anti-IL-6 (ab #2) 0.11 0.90 126 21.36 0.42 anti-IL-6 sR 0.100.89 138 40.33 0.40 anti-IL-8 (ab #1) 0.20 0.71 137 31.00 0.97 anti-IL-8(ab #2) 0.17 0.94 136 28.37 0.05 anti-IL-10 (ab #1) 0.14 0.82 138 41.390.92 anti-IL-10 (ab #2) 0.18 0.94 102 20.97 0.11 anti-KRT18 (keratin,type I 0.17 0.94 138 30.62 0.87 cytoskelatal 18) anti-Laminin (ab #1)0.11 0.93 138 38.49 0.04 anti-Laminin (ab #2) 0.16 0.90 138 37.99 0.18anti-Lipase 0.18 0.90 138 35.04 0.49 anti-M2-PK-(pyruvate kinase type0.19 0.80 127 19.77 0.80 M2) anti-MCP-1 (monocyte 0.12 0.93 138 50.750.28 chemoatractant protein) anti-MPM2(Ser/Thr, Pro 0.18 0.67 138 36.530.26 phosphorylated mitotic protein) anti-Ornithine decarb oxylase 0.130.83 138 46.81 0.92 anti-PAI-1 (plasminogen activator 0.16 0.96 136 38.50.25 inhibitor type-1) anti-PIVKA-II (protein induced 0.18 0.93 13840.45 0.82 vitamin K antagonist) anti-Plasminogen 0.16 0.81 138 40.611.04 anti-Serum amyloid A 0.20 0.88 138 28.34 0.09 anti-sTNFR (solubletumor 0.19 0.82 133 18.70 0.05 necrosis factor receptor 1)anti-Thioredoxin 0.17 0.75 138 45.15 1.75 anti-TIMP-1 (tissue inhibitorof 0.15 0.90 138 30.73 0.53 matrix metalloproteinases-1) anti-TNFα 0.160.77 138 40.08 0.67 anti-Transferrin 0.14 0.76 138 71.30 0.15anti-Troponin I 0.13 0.89 138 42.64 1.3 anti-TSP-1 (thrombospondin-1)0.11 0.88 138 20.54 −0.01As a quality control measure to confirm proper collection and treatmentof the data, independently-collected ELISA measurements from IgM andhemoglobin were compared to the microarray measurements. Thecorrelations for IgM were 0.74, 0.69, 0.74 and 0.76 for sets one throughfour, respectively, and the correlations for hemoglobin were 0.79, 0.84,0.76 and 0.83 for sets one through four, respectively, which weresufficient confirmation of data quality.

Sets one & two used a hydrogel that was activated withN-hydroxysuccinimide functional groups to provide covalent linkage ofthe spotted proteins and antibodies. Sets three & four used glassmicroscope slides that were each coated with an ultra-thin layer ofnitrocellulose. Representative scans of the microarrays show somedifferences between the two surfaces (FIG. 1A-D).

FIG. 1 shows arrays from sets one & two, performed on hydrogels in (A)and (C), and arrays from sets three & four, performed on nitrocellulosein (B) and (D). The arrays were incubated either with labeled serum (A &B) or unlabeled serum (C & D), which served as negative controls. Thespacing between the spots is 300 μm in A & C and 240 μm in B & D. Theantibodies and proteins were spotted in triplicate. The top left tripletand the bottom row were control spots: digoxigenin-labeled andbiotin-labeled BSA. The microarray scanner settings and the imagebrightness and contrast settings were optimally chosen for each surfacetype, with slight differences between the settings for A & C and thesettings for B & D.

Both surfaces showed good signal-to-background ratios. The hydrogelsurface often showed abnormal spot morphologies—incomplete spots orrings around the spots. Very small spots also were sometimes present, asshown in FIG. 1A. Negative controls were performed on each surface(FIGS. 1C and 1D), in which arrays that had been incubated withunlabeled serum samples were processed normally (see the “Control S/B543” columns in Table 2). Two antibodies, anti-132 microglobulin andanti-alpha-1-antichymotrypsin, showed strong signals in both colorchannels in each negative control experiment, presumably due tointeractions between these antibodies and the detection antibodies.These two antibodies were removed from subsequent analyses.

Reproducibility between replicate experiments is critical to theeffectiveness of protein profiling experiments and can be used as ameans to filter out unreliable antibodies. Miller, J. C., Zhou, H.,Kwekel, J., Cavallo, R., Burke, J., Butler, E. B., Teh, B. S., and Haab,B. B. Antibody microarray profiling of human prostate cancer sera:antibody screening and identification of potential biomarkers.Proteomics, 3: 56-63, 2003. The reproducibility of each antibody wasassessed by calculating both the average coefficient of variation (CV)and the correlation between duplicate experiment sets, for sets one &two and for sets three & four (Table 2, columns 5 and 10 for the CVs,columns 6 and 11 for the correlations). The median CV was lower in setsthree & four than in sets one & two (0.148 and 0.162, respectively,p=0.008). The CVs are distributed from 12-22% for experiments three &four, whereas they generally range from 12-28% (with some high outliers)for experiments one & two (FIG. 2A).

FIG. 2 A-C generally shows histograms of antibody performance andcomparisons of surface types. Each histogram is a plot of the number ofantibodies (vertical axis) that have an average value within the givenbin range (horizontal axis). The light bars refer to experiment sets one& two (hydrogel surfaces), and the dark bars refer to experiment setsthree & four (thin nitrocellulose surfaces). FIG. 2A shows thecoefficient of variation (CV) for each antibody between replicate datain experiment sets one & two or between experiment sets three & four.The CVs were averaged over all the arrays in an experiment set,resulting in a single average for each antibody in each experiment sets.FIG. 2B, for each antibody, shows correlations between all themeasurements in sets one & two or between all the measurements in setsthree & four. FIG. 2C, for each antibody, shows the ratios of thebackground-subtracted signals to the backgrounds on every array, and thegeometric mean of the S/Bs over all the arrays was calculated, for bothexperiment sets one & two and three & four.

A similar trend was seen in the correlations between replicate sets(Table 1, columns 6 and 11), which were higher in sets three & four thanin sets one & two (medians 0.887 and 0.822, respectively, p<0.001). Thedistributions ranged from 0.7 to nearly 1 for sets three & four and from0.6 to nearly 1 for sets one & two, with low-correlation outliers foreach (FIG. 2B). The surfaces also were compared according to the signalstrengths of fluorescence for each of the antibodies, relative to thebackground levels from the surface surrounding the antibody spots (Table1, columns 8 and 13). The distribution of signal-to-background ratios(S/B) were weighted to higher values in the nitrocellulose experimentsin comparison to the hydrogel experiments (FIG. 2C), but notsignificantly so (medians 38 and 27, respectively, p=0.12). Theindividual antibodies can be considered independent assays, and aconsistent trend over many different antibodies gives added significanceto the observations.

The inventor next identified antibodies with significantly differentbinding patterns between the patient classes. For this and subsequentanalyses the inventor used experiment sets three & four, based on theresults of the analyses above. Sixty-nine antibodies were used in theseanalyses, after removal of control antibodies and proteins (ten),antibodies that failed gel-based quality control (nine), and antibodiesthat showed binding in the negative control experiments (two). All 69 ofthe antibodies passed a reproducibility criterion, based on a 99%confidence threshold in the correlation between sets three & four.Several antibodies showed binding levels that were statisticallydifferent (p<0.05) between each of the patient classes in bothexperiment sets three and four (Table 3). The p-value for both set 3 andset 4 are given in Table 2 for the indicated comparisons.

TABLE 2 Healthy vs. Cancer Healthy vs. Benign Cancer vs. Benign Higherin Cancer p-value Higher in Benign p-value Higher in Cancer p-valueanti-CRP (ab #2) <0.001, <0.001 anti-Alpha-1-antitrypsin (ab #2) <0.001,<0.001 anti-PIVKA-II 0.005, 0.031 anti-PIVKA-II <0.001, <0.001anti-serum amyloid A <0.001, <0.001 anti-CA15-3 0.024, 0.05 anti-Alpha-1-antitrypsin (ab #2)  0.004, <0.001 anti-CRP (ab #2) <0.001,<0.001 anti-IgA  0.014, <0.001 anti-alkaline phosphatase 0.006, 0.007anti-Alpha-1-antitrypsin (ab #1)  0.021, <0.001 anti-Cathepsin D 0.009,0.014 anti-alkaline phosphatase 0.007, 0.02  Lower in Cancer p-valueLower in Benign p-value Lower in Cancer p-value anti-Gelsolin <0.001,<0.001 anti-Plasminogen <0.001, 0.001  anti-serum amyloid A 0.006, 0.002anti-TNFα <0.001, <0.001 anti-MPM2 <0.001, 0.004  anti-MPM2 0.003, 0.002anti-M2-PK  0.009, <0.001 anti-IL-6 sR  0.005, <0.001 anti-IL-6 sR0.011, 0.002 anti-IL-8 (ab #1) 0.003, 0.004 anti-IL-8 (ab #1)  0.01,0.002 anti-Transferrin 0.001, 0.006 anti-α-2 antiplasmin 0.009, 0.005anti-Troponin T 0.016, 0.013 anti-PAI-1 0.012, 0.006 anti-Thioredoxin 0.034, <0.001 anti-CA15-3  0.01, 0.012 anti-α-2 antiplasmin 0.028,0.011 anti-Gelsolin 0.026, 0.001 anti-IGF-I 0.025, 0.045 anti-CA125 0.02, 0.018 anti-α-fetoprotein (ab #2) 0.018, 0.029 anti-CEA 0.007,0.043 anti-Transferrin  0.03, 0.023 anti-CRP (ab #1) 0.021, 0.036anti-TNFα 0.049, 0.023

A smaller number of antibodies (not shown in Table 2) were significantin either set three or set four, but not both: ten, nine, and fiveantibodies in the cancer-healthy, benign-healthy, and cancer-benigncomparisons, respectively. These antibodies may have statisticallydifferent binding levels between the groups, but the differences werenot strong enough to pass the p<0.05 threshold in both sets. Manydifferences were shared between the cancer-healthy and thebenign-healthy comparisons. Fewer antibodies distinguished the cancerand benign classes: only anti-PIVKA-II and anti-CA15-3 were consistentlyhigher, and anti-serum amyloid A was consistently lower, in cancerrelative to benign disease. The anti-CA 19-9 (ab #2) antibody was usedonly in set 4 (not shown in Table 2). This antibody was higher in cancerrelative to normal (p<0.001) and relative to benign disease (p<0.001).These analyses show the extensive differences between the diseaseclasses and the healthy class, in addition to the considerablesimilarity between the cancer and benign classes. Correlation in thebinding profiles of the samples and the antibodies can be examined incluster image maps (Eisen, M. B., Spellman, P. T., Brown, P. O., andBotstein, D. Cluster analysis and display of genome-wide expressionpatterns. Proc. Natl. Acad. Sci. USA, 95: 14863-14868, 1998) for all theantibodies and for only the antibodies that are different between thepatient classes.

TABLE 3 Method 1 Method 2 Method 3 Healthy vs. Cancer anti-CRP (ab #2)anti-α-2 antiplasmin anti-Alpha-1- anti-Gelsolin anti-Alpha-1-antitrypsin (ab #2) anti-IgA antitrypsin (ab #1) anti-CRP (ab #2)anti-IL-1β anti-Alpha-1- anti-Gelsolin anti-IL-6 (ab #1) antitrypsin (ab#2) anti-PIVKA-II anti-Cathepsin D anti-Plasminogen anti-Complement C5anti-Troponin T anti-CRP (ab #2) anti-Gelsolin anti-Haptoglobin anti-IgAanti-IL-6 (ab #1) anti-PIVKA-II anti-Plasminogen anti-Troponin T Healthyvs. Benign anti-Alkaline phosphatase anti-α-2 antiplasmin anti-CRP (ab#2) anti-CRP (ab #2) anti-CRP (ab #2) anti-Plasminogen anti-PAI-1anti-IgA anti-Plasminogen anti-Plasminogen anti-Serum amyloid Aanti-TIMP-1 Cancer vs. Benign None anti-Serum amyloid A anti-CRP (ab #1)anti-Serum amyloid A

FIG. 8 shows two-way hierarchical clustering of the microarray data fromset 4. Microarray data from 138 serum samples (horizontal axis) and 71reproducible antibodies (vertical axis) were logged and median centeredprior to clustering. Independently-collected ELISA measurements areincluded for the proteins IgM and hemoglobin. The total proteinconcentration of each serum sample was also included in the cluster.

FIG. 9A-C shows two-way hierarchical clustering of antibody responsethat discriminates between patient classes in both experiment set 3 andexperiment set 4 with p<0.05. Higher antibody response in cancerpatients versus healthy controls, benign patients versus healthycontrols, and cancer patients versus benign patients are indicated forFIGS. 9A, 9B, and 9C, respectively.

The inventor examined other relationships between the samples. Thebenign-class samples consisted of 15 samples from chronic pancreatitispatients and 15 samples from acute pancreatitis patients. Anti-CRP (ab#2) and anti-serum amyloid A were higher in acute pancreatitis in bothsets three (p=0.005 and 0.04, respectively) and four (p=0.009 and 0.003,respectively), and anti-alpha2 macroglobulin was higher in chronicpancreatitis in sets three & four (p=0.06 and 0.02, respectively). Thus,using these antibodies, acute and chronic pancreatitis samples are verysimilar except for major acute phase reactants. Precise staginginformation was available on the cancer samples from ENH, and thesamples were grouped by stage I and II (n=13) and stage III and IV(n=23). Four antibodies (anti-troponin T, anti-CD26, anti-IL-10, andanti-IL1α) had binding levels lower in the early stage patients, andnone were higher, in both sets three & four. Some antibodies wereelevated relative to healthy only in early stage cancer (anti-cathepsinD) or only in late stage cancer (anti-alpha-1-antitrypsin (ab #2),anti-von Willebrand factor, and anti-IgA). Thus variation in certainprotein levels may occur in association With cancer stage.

As outlined recently (Ransohoff, D. F. Bias as a threat to the validityof cancer molecular-marker research. Nat Rev Cancer, 5: 142-149, 2005),the design of experiments comparing groups of specimens must becarefully examined to identify potential sources of bias that couldproduce misleading results. Ideally, the patient classes should haveequivalent demographic characteristics, the samples should be acquiredand handled using precisely the same protocols, and the experimentalprocessing of the samples should be randomized. The samples were treatedequivalently once assembled at the VARI, but sources of bias could havebeen introduced prior to their being assembled. A demographic analysisof the samples showed that the gender distributions were statisticallysimilar between the classes, but the healthy control subjects wereyounger than both other classes, and the benign class was younger thanthe cancer class (Table 6 herein below).

To gain more insight into the potential role of age in introducingsystematic variation, the inventor divided samples from the same classand same site by age—separating the top third from the bottom third—andperformed a t-test analysis between the groups. The control samples fromWM, the cancer samples from ENH, and the benign samples from ENH wereexamined, using data from both sets three & four. The mean and standarddeviation ages of the older and younger subjects in each group were53.8+/−6.6 and 27.1+/−3.4 (controls); 81.6+/−3.8 and 60.9+/−5.3(cancer); and 73.4+/−6.1 and 37.0+/−10.5 (benign). Each group had onlyone antibody with a significant difference (p<0.05) in both experimentsets between the samples from the older and younger patients.Anti-ceruloplasmin was higher in the younger control subjects,anti-plasminogen was higher in the younger cancer subjects, andanti-alpha2-macroglobulin was higher in the older patients with benigndisease. Of these antibodies, only anti-plasminogen was present in Table3. This analysis does not conclusively rule out the influence of age,but for the antibodies used in these experiments, age differencesbetween the classes do not seem to be a major source of potential biasin the comparisons.

Most of the samples were collected from ENH, but additional samples fromthe pancreatic cancer and healthy control classes were collected from WMand UM, respectively. The inclusion of cancer and control samples fromdifferent sites could potentially introduce bias, perhaps due to effectscaused by differences in collection and handling procedures. To examinethis issue further, the inventor compared the binding profiles betweensamples that were of the same patient class but from different sites. Inthe comparison of the cancer samples from ENH and WM, only one antibodyshowed a statistical difference (p<0.05) in mean protein binding. Thisantibody (anti-haptoglobin) did not appear in Table 3. Five antibodieshad statistically different binding levels between the control samplesfrom ENH and the control samples from UM in both experiment sets three &four. The trend was not consistent—two were higher in the ENH samplesand three were lower. None of these antibodies appeared in Table 3.Also, no significant differences in serum total protein concentrationsexisted between the controls from the different sites or the cancersamples from the different sites. Therefore systematic variation betweenthe samples collected at different sites also seems to have not played alarge role in defining the observed differences between the sampleclasses.

The validation and characterization of the binding properties of theantibodies is critical to the interpretation and use of the antibodymicroarray profiles. Nine antibodies were selected for western blotanalysis from those with the highest significance in Table 3, especiallythose higher in the cancer or benign classes. For each antibody, serumsamples were chosen according to the binding level in the microarraydata: two-to-four samples that showed high binding and two-to-foursamples that showed low binding to that antibody. Representative lanesfrom the immunoblots revealed the level of concordance with themicroarray data (FIG. 3). FIG. 3 depicts a characterization of selectedantibodies using Western blot analysis. Fifty pig of serum proteins frompatients exhibiting high (H) microarray reactivity or low (L) microarrayreactivity for the indicated antibody were electrophoresed onpolyacrylamide gels. The separated proteins were transferred tonitrocellulose and the antigens were detected with 10 μg/ml ofbiotinylated antibody followed by peroxidase-conjugated streptavidin andchemiluminescent development. The diamond in each blot indicates theposition of the 50 kDa molecular weight marker. Results shown arerepresentative of four patient samples run in at least two differentexperiments.

For the antibodies shown in FIG. 3, the trends observed in themicroarray data were observed in the immunoblot, and the bands appearedat molecular weights consistent with each antibody's target, withminimal additional bands.

The blot of anti-PIVKA-II also confirmed the trends in the microarraydata. The blots of anti-plasminogen and anti-alkaline phosphatase failedto showed bands at the expected molecular weights, perhaps due to afailure of the antibody to recognize the denatured target. The blot ofanti-alpha-1-antitrypsin (ab #1) showed the correct size band, but thedifferences between the high and low serum samples were too subtle toverify on the blot.

Other antibodies were tested by probing their response to dilutions ofpurified cognate antigens. Purified proteins corresponding to theanalytes of ten of the antibodies were spiked in to either human serumor BSA at various concentrations. These mixes were labeled, mixed with adifferentially-labeled reference solution, and analyzed on antibodymicroarrays. The correspondence between the observed changes in signalintensity at each antibody and the expected changes, based on the knownanalyte concentrations, indicates the accuracy of the antibody binding(FIG. 4).

FIG. 4A-C shows antibody binding validation using analyte dilutions. Theratio (log₂) of sample-specific fluorescence to reference-specificfluorescence at a given antibody was plotted with respect to theconcentration of the respective analyte. FIG. 4A shows Anti-CEAmeasuring purified CEA spiked into human serum. FIG. 4B showsAnti-lipase measuring purified lipase spiked into human serum. FIG. 4CshowsAnti-complement C3 measuring purified complement C3 spiked intohuman serum. FIG. 4D shows Anti-complement C3 measuring purifiedcomplement C3 spiked into a BSA solution. FIG. 4E shows Anti-CRP (ab #2)measuring recombinant CRP spiked into a BSA solution. FIG. 4F showsAnti-hemoglobin measuring purified hemoglobin spiked into a BSAsolution. FIG. 4G shows Anti-IgG-Fc measuring purified IgG spiked into aBSA solution. FIG. 4H shows Anti-IgM measuring purified IgM spiked intoa BSA solution. FIGS. 4A-D show the mean of duplicate experiments, andthe error bars are the standard deviations between the duplicates. Thedashed lines are the known ratios of sample:reference analyteconcentrations. For the analytes spiked into serum (FIGS. 4A-C), theendogenous serum concentration of that analyte was estimated, and thesample:reference ratio was calculated by (C_(E)+C_(S))/C_(E), whereC_(E) is the endogenous concentration and C_(S) is the spiked-inconcentration of the analyte.

Each of the antibodies of FIG. 4 showed binding that varied inaccordance with the changes in analyte concentrations. When analyteswere spiked into serum (FIG. 4A-C), the binding response of the antibodywas curved, reflecting the endogenous levels of that analyte in theserum, and when the analytes were spiked into BSA (FIG. 4D-H), thebinding response was linear. Complement C3 was diluted into both serum(FIG. 4C) and BSA (FIG. 4D). Anti-cathepsin D also showed a linearresponse with analyte concentration (not shown). Anti-plasminogen onlyshowed a response at high analyte concentrations, andanti-alpha-1-antitrypsin (ab #1) did not show binding of the analyte(not shown). These studies confirm binding of the cognate analytes, butdo not necessarily confirm specific binding on the microarray,especially if endogenous serum concentrations are below what wasmeasured here. For example, CEA is normally present in the serum atconcentrations around 10 ng/ml, below the range of the dilution series.

Thus the inventor positively confirmed the binding trends of seven ofthe nine antibodies tested by western blot and eight of the tenantibodies tested by antigen dilutions. Eight antibodies thatdiscriminated the patient groups (anti-CA 19-9 (ab #2), anti-cathepsinD, anti-CRP (ab #2), anti-gelsolin, anti-serum amyloid A, anti-PIVKA-II,and anti-alpha-1-antitrypsin (ab #1)) were validated, and the accuracyof two others, anti-IgA and anti-transferrin, had been confirmedpreviously by comparisons to ELISA measurements (Hamelinck, D., Zhou,H., Li, L., Verweij, C., Dillon, D., Feng, Z., Costa, J., and Haab, B.B. Optimized normalization for antibody microarrays and application toserum-protein profiling. Mol Cell Proteomics, 2005).

An advantage of multiplexed analysis is that one may examine coordinatedpatterns of expression and explore algorithms for combining “weak”individual classifiers into a “powerful” combined classifier, which mayincrease the accuracy of sample classification. Three methods weretested for the classification: a boosting decision tree, boostinglogistic regression, and logistic regression with forward selection(methods 1-3, respectively). Classifiers were made using each method todistinguish cancer from healthy, benign from healthy, and cancer frombenign, using data from 77 antibodies in experiment sets three & four.

The average sensitivities, specificities and error rates from the crossvalidations were compared for each method (Table 4).

TABLE 4 Set 3 Healthy vs. Cancer Number of Method SensitivitySpecificity Error Antibodies 1 0.813 0.920 0.139 25 2 0.880 0.895 0.11328 3 0.900 0.896 0.102 10 Healthy vs. Benign Number of MethodSensitivity Specificity Error Antibodies 1 0.842 0.890 0.129 6 2 0.8080.920 0.127 13 3 0.967 1.000 0.013 6 Cancer vs. Benign Number of MethodSensitivity Specificity Error Antibodies 1 0.650 0.600 0.366 2 2 0.8830.483 0.258 4 3 0.949 0.800 0.101 11 Set 4 Healthy vs. Cancer Number ofMethod Sensitivity Specificity Error Antibodies 1 0.863 0.895 0.121 14 20.950 0.895 0.074 20 3 0.933 0.938 0.065 6 Healthy vs. Benign Number ofMethod Sensitivity Specificity Error Antibodies 1 0.717 0.895 0.178 14 20.758 0.940 0.135 17 3 0.970 0.979 0.025 8 Cancer vs. Benign Number ofMethod Sensitivity Specificity Error Antibodies 1 0.543 0.900 0.332 1 20.830 0.483 0.290 2 3 0.916 0.742 0.145 8

The results were highly reproducible between sets three & four. Allthree methods were effective in distinguishing cancer from healthy andbenign disease from healthy, with method 3 producing the lowest errorrates in each comparison. For the distinction of cancer from benigndisease, methods 1 and 2 gave about a 30% error rate, and method 3produced about a 10% and 14% error rate in sets three and four,respectively. The relative numbers of antibodies used by each methodwere consistent between sets three & four, with method 3 using thefewest antibodies for the cancer-healthy and the benign-healthycomparisons and the most antibodies for the cancer-benign comparison.

The slight variability between sets three & four in the antibodies onthe arrays may have affected the makeup of the classifiers. For example,anti-CA 19-9 (ab #2) was used only in set four, and the resultingclassifiers shared only about half of the same antibodies between setsthree and four (Table 1 presents the common antibodies used in setsthree & four). However, the classifiers were robust to these changes, asthey performed equally well in both sets. Further, the performance wasnot diminished when a classifier from one set was applied to the otherset (not shown). The antibodies used by all three methods are likely tobe the most important for the classifications (Table 5).

TABLE 5 Cancer vs. Healthy Benign vs. Healthy Cancer vs. Benign Used byall methods, Set 3 anti-Amylase anti-CRP (ab #2) anti-PIVKA-II anti-CRP(ab #2) anti-Plasminogen anti-Serum amyloid A anti-Gelsolinanti-Plasminogen Used by all methods, Set 4 anti-Alpha-1-antitrypsinanti-CA19-9 (ab #2) anti-CA19-9 (ab #2) (ab #2) anti-CRP (ab #2)anti-CA19-9 (ab #2) anti-Gelsolin anti-CRP (ab #2) anti-M2-PKanti-Gelsolin anti-Plasminogen

These antibodies all appeared in Table 3 except for anti-amylase andanti-CA 19-9 (ab #2) for the benign-healthy comparison. Therefore, ingeneral the most important antibodies for the classifiers havesignificant differences between the samples classes.

The distributions of the measurements for the antibodies used by allthree methods in set four showed the extent of the differences betweenthe sample classes for the individual antibodies (FIG. 5). FIG. 5A-Cshows distributions of measurements for antibodies contributing to theclassifications. Antibodies that were used by all three classificationmethods are shown, using data from experiment set four. FIG. 5A showshealthy controls (light boxes) and pancreatic cancer (dark boxes). FIG.5B shows healthy controls (light boxes) and benign disease (dark boxes).FIG. 5C shows pancreatic cancer (light boxes) and benign disease (darkboxes). The boxes give the upper and lower quartiles of the measurementswith respect to the median value (horizontal line in each box). Thelines give the ranges of the measurements, excluding outliers, which arerepresented by circles. The asterisk indicates the measurements were notstatistically different (p<0.05) between the two classes. Thesedistributions show that the accuracy of the distinctions between thesample classes was improved using multiple antibodies as compared tousing single antibodies. For example, in the comparison of cancer tohealthy, the most significant individual antibodies were anti-CRP (ab#2) and anti-CA 19-9 (ab #2), which at a fixed specificity of 90% hadsensitivities of 74% and 55%, respectively, less than the valuesachieved using multiple antibodies (Table 4). The best individualantibody in the comparison of cancer to benign disease was CA 19-9 (ab#2) at 90% specificity and 59% sensitivity, again less than theperformance of combined antibodies.

Biomarkers

Because of the low prevalence of pancreatic cancer, large collections ofcomplete and carefully controlled samples for pancreatic cancerbiomarker research are rare. By necessity the samples studied wereassembled from three different sites. While the findings cannotconclusively be determined to be free from bias, analysis of the effectsof age and acquisition site on the microarray profiles seemed toindicate that the observed differences between the sample classes didnot arise artifactually from these sources. Further, many of the trendswere consistent with previous research, as described below, lendingfurther support to the validity of the comparisons.

The binding specificities of antibodies must be confirmed beforeconclusions can be made about changes in the levels of the targetproteins. The inventor selected antibodies for confirmation after firstscreening many antibodies and identifying reproducible measurements thatgave statistically valuable information. This strategy is complementaryto that of first characterizing the specificities and sensitivities ofall antibodies prior to running experiments. The pre-validation approachis useful when pursuing specific targets and hypotheses, but thepost-validation approach is better for screening and discovery, sincemany antibodies may be efficiently tested.

The inventor has demonstrated two complementary methods, immunoblots anddilutions of purified antigens, to further characterize antibodyperformance. Immunoblots give a good picture of binding specificities incomplex samples and are useful when purified antigens are not readilyavailable, although the results may not perfectly correspond to themicroarray results, since samples are denatured in the immunoblot assayand are native in the microarray assay. Denaturation may causeadditional or weaker binding of some antibodies. Antigen dilutions giveuseful information on antibody binding characteristics, althoughpurified antigen may not be readily available for every antibody. Theantibodies tested here by that method generally showed a response thatvalidated the binding of the intended target. In one case, withanti-alpha-1-antitrypsin (ab #1), the dilution experiment failed toproduce results, but the immunoblot confirmed binding to the proper sizeband. The high rate of success of the inventor's validations (70-80%)may reflect the robustness of the microarray assay but also may be dueto the non-random selection of the antibodies for validation. Theinventor chose antibodies that showed reproducible binding patterns andchanges between samples, which probably are more likely to be bindingthe correct target, although not necessarily exclusively. Other methodsmay give additional, complementary information that will be useful forfurther antibody characterization and validation, such asmass-spectrometry analysis of proteins bound to immunoprecipitatedantibodies.

The data of the present invention are useful to evaluate the benefit ofusing multiple antibodies for sample classification and for identifyingthe antibodies that are most important in defining signatures for thesample classes. The benefit of using multiple antibodies for theclassifications was shown in the improvement in the classificationaccuracy relative to the use of single antibodies. The low error rate inthe distinction of the cancer class from the healthy class and thebenign class from the healthy class reflect the major changes occurringin the blood of both types of disease. The distinction of cancer frombenign disease is more difficult, as those two classes can have manysimilar clinical, pathological and molecular manifestations. Pancreaticcancer is often associated with pancreatitis, and the diagnosis ofcancer in the presence of pancreatitis often presents a challenge forphysicians. Therefore the ability to accurately diagnose the two classesusing a molecular blood test is especially valuable. The classificationby the logistic regression with forward selection method (method 3)—anerror rate of only 10% and 14% in sets three & four, respectively—wasimproved over the performance of the individual antibodies and showed itmay be useful to distinguish benign from malignant disease usingbiomarkers. The choice of which antibodies to use to distinguishmalignant disease from benign cancer will be based on the results fromthis invention. Promising new markers include macrophage inhibitorycytokine 1 (Koopmann, J., Buckhaults, P., Brown, D. A., Zahurak, M. L.,Sato, N., Fukushima, N., Sokoll, L. J., Chan, D. W., Yeo, C. J., Hruban,R. H., Breit, S, N., Kinzler, K. W., Vogelstein, B., and Goggins, M.Serum macrophage inhibitory cytokine 1 as a marker of pancreatic andother periampullary cancers. Clin Cancer Res, 10: 2386-2392, 2004) andosteopontin (Koopmann, J., Fedarko, N. S., Jain, A., Maitra, A.,Iacobuzio-Donahue, C., Rahman, A., Hruban, R. H., Yeo, C. J., andGoggins, M. Evaluation of osteopontin as biomarker for pancreaticadenocarcinoma. Cancer Epidemiol Biomarkers Prey, 13: 487-491, 2004).

The validated differences observed between the samples classes includedboth previously-observed and newly-observed trends. CA 19-9 is awell-known pancreatic cancer marker, defined by a monoclonal antibodyrecognizing the sialylated Lewis_(a) blood group antigen. Reports on theperformance of CA 19-9 have varied broadly. A meta analysis of CA 19-9serum studies found a mean sensitivity and specificity for pancreaticcancer range of 81% and 91%, respectively (Steinberg, W. The clinicalutility of the CA 19-9 tumor-associated antigen. Am J Gastroenterol, 85:350-355, 1990). The observed specificity and sensitivity for pancreaticcancer using CA 19-9 alone were lower than those observations, perhapsdue to a lack of optimization of this assay for that particular analyte.Several of the observed alterations represent an acute phase response,which is typically associated with advanced pancreatic cancer (Barber,M. D., Ross, J. A., Preston, T., Shenkin, A., and Fearon, K. C. Fishoil-enriched nutritional supplement attenuates progression of theacute-phase response in weight-losing patients with advanced pancreaticcancer. J Nutr, 129: 1120-1125, 1999), and would include the elevatedCRP, serum amyloid A and alpha-1-antitrypsin and the decreasedtransferrin levels. The elevated IgA in the serum may be due toincreased secretion and leakage from the pancreatic juice, in whichelevated IgA has been associated with cancer (Goodale, R. L., Condie, R.M., Dressel, T. D., Taylor, T. N., and Gajl-Peczalska, K. A study ofsecretory proteins, cytology and tumor site in pancreatic cancer. AnnSurg, 189: 340-344, 1979).

Cathepsin D could be involved in the cancer cell invasion process(Tedone, T., Correale, M., Barbarossa, G., Casavola, V., Paradiso, A.,and Reshkin, S. J. Release of the aspartyl protease cathepsin D isassociated with and facilitates human breast cancer cell invasion. FasebJ, 11: 785-792, 1997), and its level in serum previously has beenassociated with prostate cancer (Miyake, H., Hara, I., and Eto, H.Prediction of the extent of prostate cancer by the combined use ofsystematic biopsy and serum level of cathepsin D. Int J Urol, 10:196-200, 2003); hepatocellular carcinoma (Tumminello, F. M., Leto, G.,Pizzolanti, G., Candiloro, V., Crescimanno, M., Crosta, L., Flandina,C., Montalto, G., Soresi, M., Carroccio, A., Bascone, F., Ruggeri, I.,Ippolito, S., and Gebbia, N. Cathepsin D, B and L circulating levels asprognostic markers of malignant progression. Anticancer Res, 16:2315-2319, 1996); and in benign but not malignant pancreatic disease(Tumminello, F. M., Leto, G., Pizzolanti, G., Candiloro, V.,Crescimanno, M., Crosta, L., Flandina, C., Montalto, G., Soresi, M.,Carroccio, A., Bascone, F., Ruggeri, I., Ippolito, S., and Gebbia, N.Cathepsin D, B and L circulating levels as prognostic markers ofmalignant progression. Anticancer Res, 16: 2315-2319, 1996; and Leto,G., Tumminello, F. M., Pizzolanti, G., Montalto, G., Soresi, M.,Carroccio, A., Ippolito, S., and Gebbia, N. Lysosomal aspartic andcysteine proteinases serum levels in patients with pancreatic cancer orpancreatitis. Pancreas, 14: 22-27, 1997), in contrast to this study. Itsassociation with cancer was only moderate, as it did not distinguish thecancer class from the benign class.

Gelsolin in the plasma has an actin scavenging function, and its levelin the serum can be reduced in response to acute tissue injury (Ito, H.,Kambe, H., Kimura, Y., Nakamura, H., Hayashi, E., Kishimoto, T.,Kishimoto, S., and Yamamoto, H. Depression of plasma gelsolin levelduring acute liver injury. Gastroenterology, 102: 1686-1692, 1992),presumably due to an increased binding and clearance of shed actin. Itsaltered level, which has not before been associated with pancreaticcancer or pancreatitis, may indicate a higher-than-normal amount of cellbreakdown products in the blood. That observation would be consistentwith the nature of fibrosis in pancreatic cancer, which is similar to acontinual cell breakdown and wound healing process.

Another notable observation was the decrease in several antibodiesdefined by carbohydrate epitopes, such as anti-CEA, anti-CA 15-3, antiM2-PK, and anti-CA 125, seen in association with benign disease. Thespecificities of these measurements have not been validated, but theirconsistency suggests some alteration in glycoproteins associated withbenign disease. These results show a wide variety of alterations in thesera of cancer patients, reflecting inflammation, immune responses,fibrosis, and perhaps altered glycosylation and glucose regulation.

PIVKA-II Biomarker

An unexpected finding was the elevation of protein-induced vitamin Kantagonist II (PIVKA-II) in association with pancreatic cancer. PIVKA-II(also known as des-carboxy prothrombin or DCP) is a non-functionalversion of prothrombin produced by a failure of the vitamin-K-dependentaddition of carboxylic acid to the gamma carbon of certain glutamic acidresidues. Its blood level is elevated in association with hepatocellularcarcinoma (Weitz, I. C. and Liebman, H. A. Des-gamma-carboxy (abnormal)prothrombin and hepatocellular carcinoma: a critical review. Hepatology,18: 990-997, 1993) and in response to vitamin K deficiency (Ferland, G.,Sadowski, J. A., and O'Brien, M. E. Dietary induced subclinical vitaminK deficiency in normal human subjects. J Clin Invest, 91: 1761-1768,1993), but it was not before known to be associated with pancreaticcancer. Vitamin-K-dependent alterations have been associated withglucose tolerance (Sakamoto, N., Wakabayashi, I., and Sakamoto, K. Lowvitamin K intake effects on glucose tolerance in rats. Int J Vitam NutrRes, 69: 27-31, 1999), and the alpha cells of the pancreas have theability to produce prothrombin (Stenberg, L. M., Nilsson, E., Ljungberg,O., Stenflo, J., and Brown, M. A. Synthesis of gamma-carboxylatedpolypeptides by alpha-cells of the pancreatic islets. Biochem BiophysRes Commun, 283: 454-459, 2001), so this observation could relate toalterations in glucose regulation that are commonly seen in pancreaticcancer patients.

As used by the inventor, two antibodies for detecting PIVKA-II includedAnti-Protein Induced Vitamin K Antagonist made by Crystal Chem Inc.,(Catalog No. 08040, Clone No. PIM-55), and Anti-PIVKA-II, made by USBio(Catalog No. P4210-50, Clone No. 8.F.228).

Levels of DCP in serum are considered to be “high” when the levelexceeds a threshold of three standard deviations above the mean of thelevels in a healthy control population. As shown in FIGS. 6 and 7,comparing healthy to pancreatic cancer samples, total DCP is high inpancreatic cancer compared to healthy individuals. In addition, as shownin FIGS. 6 and 7, comparing healthy individuals to cancer patients,glycosylation levels of DCP were significantly higher in cancer versushealthy (more pronounced than shown with respect to total protein).

Method for Detecting Glycosylation Levels on Multiple, Specific Proteins

Here, the inventor presents the use of labeled glycan-binding proteinsfor multiplexed detection of the levels of specific glycans on multipleproteins captured by antibody microarrays. The inventor also hasdiscovered a means to efficiently and conveniently measure the levels ofparticular glycan structures or groups on many different specificproteins using low sample volumes. Since antibody arrays of the presentinvention can be performed in a high-throughput fashion, the presentmethod has the advantage of obtaining data from large sets of samples,so that the variation in glycan levels across many samples on manyproteins can be profiled. By using different glycan-binding proteins(e.g., lectins or antibodies) for detection of glycosylated proteinscaptured on microarrays, the variation in different glycan structurescan be probed (e.g., the patterns of different glycan structurescompared). The present invention is useful for determining the proteinsthat contain particular structures.

As discussed below, the profiling of glycan variation, using the lectinsAAL and WGA, over serum samples from pancreatic cancer patients andcontrol subjects showed multiple proteins with potentialcancer-associated glycan elevations or reductions. The patterns ofreactivity from the two lectins were very different from each other,indicating distinct mechanisms of regulation for those glycans. Parallelglycan-detection and sandwich assays showed that glycans reactive withthe CA 19-9 monoclonal antibody were elevated on the proteinscarcinoembryonic antigen and MUC1 in sera from pancreatic cancerpatients, which could influence the binding and functional properties ofthose proteins. As demonstrated by these data, antibody arrays withglycan detection are highly effective for profiling variation inspecific glycans on multiple proteins and should be useful in diverseareas of glycobiology research.

Lectins are the preferred glycan binding proteins used in the method ofthe invention. Lectins include carbohydrate-binding proteins from manysources regardless of their ability to agglutinate cells. Lectins havebeen found in many organisms, including, plants, viruses, microorganismsand animals. Most known lectins are multimeric, with non-covalentlyassociated subunits, and this multimeric structure gives lectins theirability to agglutinate cells or form precipitates with glycoconjugatessimilar to antigen-antibody interactions. A common characteristic oflectins is that they bind to specifically defined carbohydratestructures. Because of this specificity that each lectin has for aparticular carbohydrate structure, even oligosaccharides with identicalsugar compositions can be distinguished. Some lectins bind onlystructures with mannose or glucose residues, while others recognize onlygalactose residues. Some lectins bind only if a particular sugar is in aterminal non-reducing position in the oligosaccharide, while others bindsugars within the oligosaccharide chain. Further, some lectins do notdiscriminate when binding to a and b anomers, while other lectinsrequire the correct anomeric structure and a specific sequence ofsugars. Thus, the binding affinity between a lectin and its receptor mayvary greatly in view of seemingly small changes in the carbohydratestructure of the receptor.

Lectins have been valuable glycan affinity reagents in experimentalformats such as affinity chromatography and electrophoresis (van Dijk,W., Havenaar, E. C. & Brinkman-van der Linden, E. C. Alpha 1-acidglycoprotein (orosomucoid): pathophysiological changes in glycosylationin relation to its function. Glycoconj J 12, 227-33 (1995)), detectionof blots of separated glycoproteins (Okuyama, N. et al. Fucosylatedhaptoglobin is a novel marker for pancreatic cancer: A detailed analysisof the oligosaccharide structure and a possible mechanism forfucosylation. Int J Cancer (2005)), and in the capture or detection ofproteins in microtiter plates to quantify glycans on specific proteins(Thompson, S., Stappenbeck, R. & Turner, G. A. A multiwelllectin-binding assay using lotus tetragonolobus for measuring differentglycosylated forms of haptoglobin. Clin Chim Acta 180, 277-84 (1989);Parker, N. et al. A new enzyme-linked lectin/mucin antibody sandwichassay (CAM 17.1/WGA) assessed in combination with CA 19-9 and peanutlectin binding assay for the diagnosis of pancreatic cancer. Cancer 70,1062-8 (1992)). Antibodies also have been developed to target and studyparticular carbohydrates, such as the cancer-associateThomsen-Friedenreich antigens (Kjeldsen, T. et al. Preparation andcharacterization of monoclonal antibodies directed to thetumor-associated O-linked sialosyl-2-6 alpha-N-acetylgalactosaminyl(sialosyl-Tn) epitope. Cancer Res 48, 2214-20 (1988); Santos-Silva, F.et al. Thomsen-Friedenreich antigen expression in gastric carcinomas isassociated with MUC1 mucin VNTR polymorphism. Glycobiology 15, 511-7(2005)) or the Lewis blood group structures (Lucka, L. et al.Identification of Lewis x structures of the cell adhesion moleculeCEACAM1 from human granulocytes. Glycobiology 15, 87-100 (2005)).Cancer-associated glycosylation has been identified using lectins onproteins such as alpha-fetoprotein (Aoyagi, Y. et al. The fucosylationindex of serum alpha-fetoprotein as useful prognostic factor in patientswith hepatocellular carcinoma in special reference to chronologicalchanges. Hepatol Res 23, 287 (2002); Shimizu, K. et al. Comparison ofcarbohydrate structures of serum alpha-fetoprotein by sequentialglycosidase digestion and lectin affinity electrophoresis. Clin ChimActa 254, 23-40 (1996)), haptoglobin (Okuyama, N. et al. Fucosylatedhaptoglobin is a novel marker for pancreatic cancer: A detailed analysisof the oligosaccharide structure and a possible mechanism forfucosylation. Int J Cancer (2005); Thompson, S., Cantwell, B. M.,Cornell, C. & Turner, G. A. Abnormally-fucosylated haptoglobin: a cancermarker for tumour burden but not gross liver metastasis. Br J Cancer 64,386-90 (1991)), alpha-1-acid glycoprotein (van Dijk, W., Havenaar, E. C.& Brinkman-van der Linden, E. C. Alpha 1-acid glycoprotein(orosomucoid): pathophysiological changes in glycosylation in relationto its function. Glycoconj J 12, 227-33 (1995)), and alpha-1-antitrypsin(Thompson, S., Guthrie, D. & Turner, G. A. Fucosylated forms ofalpha-1-antitrypsin that predict unresponsiveness to chemotherapy inovarian cancer. Br J Cancer 58, 589-93 (1988)). Lectin-affinity andimmuno-affinity electrophoresis and blotting methods were used toisolate particular proteins and quantify specific glycan groups on thoseproteins.

The inventor identifies two preferred embodiments of this invention: aone-color method for measuring glycan only, and a two-color method formeasuring both glycan and protein levels. FIGS. 10A and 10Bschematically depict the two formats. FIG. 10A shows lectin binding onlabeled proteins, detected in two colors. FIG. 10B shows two lectinsbinding two different glycans, detected in two colors. Both methods useantibody microarrays. In the first method, the proteins from abiological sample, such as serum or tissue extracts, are labeled with atag such as biotin or digoxigenin and incubated on an antibodymicroarray. Proteins that are recognized by an antibody on the array arecaptured by that antibody (i.e., proteins known as “serum” proteins).After washing off unbound proteins, a glycan-binding-protein (GBP) isincubated on the array. A GBP is a molecule that recognizes specificglycans. Examples are lectins or antibodies raised against particularglycan groups. The GBP is labeled with a tag that is different from theone used to label the proteins captured by the antibodies on themicroarray. The GBP will bind to glycan groups on the proteins that arecaptured by the antibody microarray, in proportion to the amount of theglycan present. After unbound GBP is washed off, the tags on the boundproteins and bound GBPs are detected using technology such as two-colorrolling circle amplification or resonance-light scattering. The relativesignal from the GBP and captured proteins reflects the amount of glycanper protein. Other tags and methods of tag detection are known in theart and can be applied to the present invention.

In another preferred embodiment of the invention, an unlabelledbiological sample (e.g., serum) is incubated on an antibody microarray,and the proteins are captured by the immobilized antibodies according totheir specificities. After unbound proteins are washed off, twodifferent GBPs are incubated on the array, each targeting a differentglycan and each labeled with a different tag from the other (e.g.,biotin and digoxigenin). Unbound GBPs are washed off, and the tags aredetected as described in the first embodiment. The relative signal fromthe two tags reflects the relative signal of the two glycan groups oneach protein and additional modification of the present invention couldinclude the detection of more than two different tags on the microarray.With the ability to detect more than two tags (e.g., using three-coloror four-color detection) additional GBPs, each labeled with a differenttag, could be used in the same experiment.

Using the present invention, glycans on multiple proteins can bemeasured in one experiment, using low sample volumes, so that efficientscreening and profiling of changes in glycosylation is possible. Samplescan be run in high throughput, which is important for biomarkerresearch. Also, the experiments are highly reproducible, anothercritical factor in characterizing clinical samples. All of theseadvantages will be valuable for biological and diagnostics research.Further, the present invention could be used as a tool for research onspecific glycans on certain proteins. The components required also couldbe assembled in a kit. If a specific glycosylation alteration associatedwith a disease is discovered that is useful for diagnostics, the presentinvention could be used as a clinical assay, also preferably in a kit.Preferably, the methods of the present invention are useful as appliedto the study of changes in glycosylation on specific serum proteins inrelation to cancer.

In addition, the inventor measured both glycan and protein levels usingparallel arrays detected either with lectins or with sandwich detectionantibodies. The parallel detection of both glycan and protein wasimportant to observe the relationships between those two levels. Theability to efficiently process many samples led to the identification ofseveral potential cancer-related glycan and protein alterations.High-throughput sample processing also allowed the comparisons ofprofiles from different lectins and the observation of differentialregulation of different glycan structures.

Since antibodies have carbohydrate groups, it occasionally happens thata given GBP will bind to the glycan group on the captured antibody onthe microarray, rather than bind to the glycan on the captured protein.This effect would limit the ability to detect glycan changes usingcertain antibody-lectin combinations. To allow the use of anyantibody-lectin pair, the inventor developed a method to block GBPbinding to glycan groups on the spotted antibodies. The method employsoxidation (e.g., with NaIO4) of the carbohydrate groups on the spottedantibodies followed by derivatization with a hydrazide-maleimidebifunctional crosslinking reagent, followed by attachment of acysteine-glycine dipeptide to the maleimide group (FIG. 12A). Theoxidation opens saccharide rings at cis-alcohol positions. One of thealcohols is oxidized to an aldehyde group. It is with that group thatthe hydrazide reacts. All antibody carbohydrate chains containsaccharide groups with cis-alcohols. The Cys-Gly dipeptide adds bulk tothe derivatization of the carbohydrates to prevent recognition bylectins (FIG. 12B). With the present method, GBP binding to theantibodies may be almost completely abolished by the conjugation of theantibody-associated carbohydrate groups with the di-peptide. With thisstep, glycans on the captured proteins will be detected rather than theglycans on the spotted capture antibodies.

In a preferred method, the antibodies are first spotted on the array,followed by the blocking steps. Alternatively, however, the antibodiesto be spotted could be blocked while in solution, i.e., before theantibodies are spotted on the microarray. This alternate blockingprocedure may be useful to pretreat the glycan-detection reagents incase it is desirable to detect more than one glycan in a single assay,using multiple lectins. Since lectins are glycoproteins, they may reactwith each other when they are used together. Blocking the glycan groupson the lectins would prevent those interactions.

Glycosylation alterations were found on the proteins haptoglobin,alpha-2-macroglobulin, MUC1, and CEA. Elevated fucosylation onhaptoglobin previously has been observed in pancreatic cancer (Okuyama,N. et al. Fucosylated haptoglobin is a novel marker for pancreaticcancer: A detailed analysis of the oligosaccharide structure and apossible mechanism for fucosylation. Int J Cancer (2005)) and indiseases such as inflammatory joint disease (Thompson, S., Kelly, C. A.,Griffiths, I. D. & Turner, G. A. Abnormally-fucosylated serumhaptoglobins in patients with inflammatory joint disease. Clin Chim Acta184, 251-8 (1989)), ovarian cancer (Thompson, S., Dargan, E. & Turner,G. A. Increased fucosylation and other carbohydrate changes inhaptoglobin in ovarian cancer. Cancer Lett 66, 43-8 (1992)), andhepatocellular carcinoma (Naitoh, A., Aoyagi, Y. & Asakura, H. Highlyenhanced fucosylation of serum glycoproteins in patients withhepatocellular carcinoma. J Gastroenterol Hepatol 14, 436-45 (1999)).Reduced fucosylation on alpha-2-macroglobulin was not before observed.Glycan alterations on MUC1 in cancer have been observed previously,including truncations in O-glycosylation that lead to the exposure ofcore carbohydrate structures such as the Thomsen-Friedenreich and sialylTn antigens (Reis, C. A., David, L., Seixas, M., Burchell, J. &Sobrinho-Simoes, M. Expression of fully and under-glycosylated forms ofMUC1 mucin in gastric carcinoma. Int J Cancer 79, 402-10 (1998); Lloyd,K. O., Burchell, J., Kudryashov, V., Yin, B. W. & Taylor-Papadimitriou,J. Comparison of O-linked carbohydrate chains in MUC-1 mucin from normalbreast epithelial cell lines and breast carcinoma cell lines.Demonstration of simpler and fewer glycan chains in tumor cells. J BiolChem 271, 33325-34 (1996)). It was previously shown that biliary andpancreatic mucins carry the sialyl-Lewis^(x) and sialyl-Lewis^(a) (theCA19-9 epitope) structures (Ho, J. J., Siddiki, B. & Kim, Y. S.Association of sialyl-Lewis(a) and sialyl-Lewis(x) with MUC-1 apomucinin a pancreatic cancer cell line. Cancer Res 55, 3659-63 (1995);Kalthoff, H., Kreiker, C., Schmiegel, W. H., Greten, H. & Thiele, H. G.Characterization of CA 19-9 bearing mucins as physiological exocrinepancreatic secretion products. Cancer Res 46, 3605-7 (1986); von Ritter,C. et al. Biliary mucin secreted by cultured human gallbladderepithelial cells carries the epitope of CA 19-9. Anticancer Res 17,2931-4 (1997)). This study provided fresh evidence that the CA19-9reactive structure is actually increased on MUC1 in cancer. CEA familymembers have been shown to carry Lewis^(x) structures (Lucka, L. et al.Identification of Lewis x structures of the cell adhesion moleculeCEACAM1 from human granulocytes. Glycobiology 15, 87-100 (2005); Stocks,S. C., Albrechtsen, M. & Kerr, M. A. Expression of the CD15differentiation antigen (3-fucosyl-N-acetyl-lactosamine, LeX) onputative neutrophil adhesion molecules CR3 and NCA-160. Biochem J 268,275-80 (1990); Stocks, S. C. & Kerr, M. A. Neutrophil NCA-160 (CD66) isthe major protein carrier of selectin binding carbohydrate groups LewisXand sialyl lewisX. Biochem Biophys Res Commun 195, 478-83 (1993)), butcancer-associated elevations of the CA 19-9-reactive structure were notdemonstrated. Other proteins from the study may have elevated glycanlevels in cancer, and further experiments will be required tocharacterize the relationships between protein and glycan levels.Further studies also could address the functional consequence of theobserved glycan alterations. Lewis blood group structures are ligandsthat mediate binding to endothelial cells through E-selectin andendothelial leucocyte adhesion molecule 1 (ELAM-1), so a possible roleof the CA 19-9-reactive elevations may be to modulate interactions withreceptors or circulating proteins.

A glycan alteration in a protein in a diseased patient (e.g., a cancerpatient) serum sample is present at a higher or lower level (i.e., at adifferent level) than in the protein in a healthy patient serum samplewhen the level of glycosylation of the particular protein in thediseased patient exceeds a threshold of one and one-half standarddeviations above the mean of the concentration as compared to thehealthy patient. More preferably, a glycan alteration in a protein in adiseased patient (e.g., a cancer patient) serum sample is present at ahigher or lower level (i.e., at a different level) than in the proteinin a healthy patient serum sample when the level of glycosylation of theparticular protein in the diseased patient exceeds a threshold of twostandard deviations above the mean of the concentration as compared tothe healthy patient. Most preferably, a glycan alteration in a proteinin a diseased patient (e.g., a cancer patient) serum sample is presentat a higher or lower level (i.e., at a different level) than in theprotein in a healthy patient serum sample when the level ofglycosylation of the particular protein in the diseased patient exceedsa threshold of three standard deviations above the mean of theconcentration as compared to the healthy patient. New markers could bediscovered using this method to probe many different cancer-associatedglycans and proteins, and the combined data from several differentglycan measurements could form specific glycan profiles that are alteredin cancer.

In some cases the data from these types of experiments could be affectedby the existence of protein-protein complexes in the captured proteins,so that the GBPs detect glycans on proteins that are in complex with thecaptured proteins, rather than on the primary target of the captureantibody. That effect could be circumvented by first treating the sampleto denature or digest proteins, or glycan alterations could be studiedfurther using gel electrophoresis and blotting methods (as shown in FIG.14). Blotting methods could be useful to follow up on particularproteins and glycans identified using the present inventions.

This novel method could be used for a variety of applications, such asto develop novel biomarkers, to characterize the glycans that arepresent of various proteins, or to investigate the coordinatedregulation of different glycan structures. The value of the experimentswill be enhanced by the better characterization of the specificities ofthe lectins and the development of new antibodies againstcancer-associated glycotopes. Glycan microarrays could be a usefulcomplementary method for that purpose (Blixt, O. et al. Printed covalentglycan array for ligand profiling of diverse glycan binding proteins.Proc Natl Acad Sci USA 101, 17033-17038 (2004); Manimala, J. C., Li, Z.,Jain, A., VedBrat, S. & Gildersleeve, J. C. Carbohydrate array analysisof anti-Tn antibodies and lectins reveals unexpected specificities:implications for diagnostic and vaccine development. Chembiochem 6,2229-41 (2005)). The use of highly-specific and well-characterizedlectins and glycan-binding antibodies, combined with high-throughputsample processing methods, should lead to a better characterization ofthe variation of carbohydrate structures in health and disease.

Total CA 19-9 Detection and Glycan Detection on Specific Proteins toDifferentiate Pancreatic Cancer from Benign Pancreatic Disease

Total CA 19-9 in serum is elevated in the majority of pancreatic cancerpatients but does not achieve the performance required for either earlydetection or diagnosis, due to both false positive and false negativereadings [4]. Patients with biliary obstruction, liver diseases, andpancreatitis may have elevations in CA 19-9, so its elevation is notexclusively specific for malignancy. In addition, some patients withcancer do not show elevation [5], reducing its usefulness for confirmingcancer in suspect cases. The information from CA 19-9 is useful, incoordination with other clinical factors, for monitoring diseaseprogression in patients receiving therapy [6].

The CA 19-9 marker is a carbohydrate antigen that is detected by amonoclonal antibody, as noted above. This carbohydrate antigen, aquatra-saccharide called sialy Lewis A, is found on multiple, differentproteins. In the sandwich ELISA method used in the clinical assay, theCA 19-9 monoclonal antibody measures the CA 19-9 antigen on manydifferent carrier proteins [7]. The identities of the carrier proteinsare not well characterized but are known to include mucins andcarcinoembryonic antigen [7,8]. Previous work showed that the mucinsMUC1, MUC5AC, and MUC16 are major cancer-associated carriers of the CA19-9 antigen in the blood [11].

The sub-optimal performance of the CA 19-9 assay may, in some cases, bedue to the appearance of the CA 19-9 carbohydrate antigen on carrierproteins that are not specific to cancer. It is possible that thecarrier proteins of the CA 19-9 antigen are different between diseasestates, as suggested earlier [9].

The inventors examined whether the detection of the CA 19-9 antigen onspecific proteins yielded improved biomarker performance over total CA19-9 in differentiating pancreatic cancer patients from pancreatitispatients, for which CA 19-9 alone does not give sufficient performanceto be clinically useful [2]. And the inventors demonstrated that cleardistinctions exist between patients in the proteins that carry the CA19-9 antigen, and that a biomarker panel based on the detection of theCA 19-9 on specific proteins identifies a greater percentage of cancerpatients than the conventional CA 19-9 assay.

The inventors used antibody arrays for glycan detection, which provideda convenient approach to measuring the CA 19-9 antigen on multiple,individual proteins. They found that the mucins MUC1, MUC5AC, and MUC16are major cancer-associated carriers of CA 19-9, but because of thediversity among patients in the proteins that carry CA 19-9, thedetection of CA 19-9 on any single protein did not out-perform total CA19-9. However, for individual patients with low CA 19-9 in which apredominant carrier was identified, selective discrimination from thepancreatitis controls was possible. As such, a combination markercomprising total CA 19-9 plus CA 19-9 on selected proteins (such asMUC1, MUC5AC, and MUC16) could yield improved sensitivity for cancerdetection over total CA 19-9 alone. This result was achieved in threeindependent sample sets from three different institutions.

New biomarkers to more sensitively distinguish cancer from benigndisease conditions could be significant in a variety of ways. A possiblearea of application would be to diagnose patients that have pancreaticabnormalities as discovered by CT scan. Several conditions in additionto malignancies produce abnormal pancreatic findings by CT [14], such ascystic lesions, pancreatitis, and common bile duct obstruction, and onlysome require further intervention. Because no molecular marker nowexists to sort out the conditions, nearly all patients go on toendoscopic ultrasound and potential biopsy. A reduction in thisinvasive, costly, and risky procedure is desirable, considering the highrate of patients with benign conditions that receive it.

The present finding of subgroups of patients based on CA 19-9 proteincarriers guides the further development of a biomarker panel. Such apanel can be used with patients having moderate and low total CA 19-9levels, since they are the hardest to distinguish from pancreatitis. Forthose that have detectable total CA 19-9, the panel could be used toidentify the predominant carrier protein of the antigen (e.g., MUC1,MUC5AC, or MUC16) because the detection of CA 19-9 on that protein canprovide selective detection of those patients, as demonstrated here. Forthose that have detectable mucin, but in which the mucins are not thepredominant CA 19-9 carriers, the detection of another glycan on themucins may provide discrimination. This situation was present in patient3607 (FIG. 3). This patient showed no CA 19-9 signal on any carrier butshowed strong signal at the MUC5AC capture antibody when detected by thelectin BPL. The glycan bound by BPL, terminal beta-linked galactose, isdistinct from the glycan bound by CA 19-9, confirming the need for thedetection of additional glycans beyond CA 19-9. This result isconsistent with the fact that certain individuals, estimated to bearound 5% of the population, are genetically deficient in an enzyme thatcompletes a critical step in the biosynthesis of the CA 19-9 antigen[15,16].

Studies of cancers of other organs have identified subcategories ofdisease defined by molecular characteristics [17], but clearsubcategories of pancreatic cancer have not emerged despite the geneexpression and molecular profiling studies that have been performed onpancreatic ductal adenocarcinoma [18,19], However, it is likely thatdefined subgroups of the disease exist that have distinct molecularcharacteristics and that produce distinct alterations in the blood.Therefore, we expect that a panel of complementary markers, such as thatdisclosed herein, each member specific for a subgroup of disease, couldbe used to detect a high percentage of patients.

The biological nature of the subgroups found here will be investigatedin future work by comparisons between blood marker status and theprimary tumors. An observation of correlations between primary-tumorcharacteristics and blood-marker subgroups could be extremely valuable.First, the blood biomarker would provide more detailed information aboutdisease status with possible implications for treatment. Second, themolecular characteristics of subgroups of primary tumors could guide thesearch for additional proteins or glycans that improve the detection ofthose subgroups. For example, if particular proteins or glycans arefound to be highly secreted in the primary tumors from the patients withlow CA 19-9 and mucin in the blood, those proteins or glycans may helpto detect those “false negative” patients. In addition, glycoproteomicsefforts could be focused on particular subpopulations, which may be moreeffective than efforts aimed at entire populations, since markerselevated only in subgroups would be diluted in the population.

Improving the limit of detection of the analytical assay may alsoenhance the ability to detect the cancer patients. Some of the patientsnot detected in this study may have mucin proteins secreted into theblood but at very low levels, which might be detectable given a verysensitive assay. Several options are available for improving thedetection limits of the assay. Amplification of the fluorescence signalis possible using rolling-circle amplification [20,21] or tyramidesignal amplification [22]. A novel format that restricts the sample toultra-low detection volumes can lower detection limits usingenzyme-based chemiluminescence detection [23]. A new generation ofelectrochemical biosensors is achieving or surpassing detection limitsachieved by fluorescence [24], which provides another possible route forthe improved detection of cancer patients.

The approach to biomarker development demonstrated here may be useful inother biomarker applications. The detection of glycans on specificproteins may yield greater accuracy for a variety of disease states thanby detecting just protein levels, as with standard immunoassays, or justthe levels of a particular glycan on all proteins, as with theconventional CA 19-9 assay. The antibody-lectin sandwich array providesan ideal format for testing combinations of proteins and glycans forsuch investigations [25]. The proteins and glycans to be targeted on thearrays can be derived from known molecular alterations, such as mucinsin pancreatic cancer [9,26,27], or from genomics, proteomics, andglycoproteomics studies. Glycoproteomics methods used in combinationwith antibody arrays could represent a powerful strategy for biomarkerdevelopment [28], the former providing potential new proteins andglycans to test, and the latter providing an efficient and accuratemeans of testing multiple candidates.

The present invention provides a basis for the early diagnosispancreatic cancer. An improvement over the conventional total CA 19-9assay is achieved by detecting glycan levels (e.g., the level of CA 19-9antigen) on specific mucins (e.g., MUC1, MUC5AC, and MUC16) rather thanon all protein carriers. Here, the inventors showed that the sensitivityof cancer detection was raised to 84-100% from 79-84%, at 75%specificity, in three independent sample sets from three differentinstitutions. The identification of subgroups of patients based on CA19-9 carrier status suggests biologically distinct entities of thedisease that can be detected by complementary markers.

In certain aspects, the invention provides assays and methods todiscriminate pancreatic cancer from benign pancreatic diseases, such as,cystic lesions, pancreatitis, and common bile duct obstruction. That is,the methods and assays are used to diagnose pancreatic cancer, and morespecifically, to diagnose pancreatic cancer rather than a benignpancreatic disease. In some embodiments, these methods and assays detectthe glycan alterations on one or mucin proteins, e.g., MUC1, MUC5AC, andMUC 16, as complementary biomarkers to total CA 19-9 levels. Any assaythat will detect total CA 19-9 and mucin glycan levels can be used,whether assayed individually (e.g., by sandwich ELISA or other methodsknown in the art) or by high throughput methods (e.g., by using antibodyarrays such as those described herein).

Moreover, any combination of assays to detect glycan alterations on themucins can be used; and a glycan binding protein other than themonoclonal antibody to CA 19-9 can be used to detect these glycans. Forexample, total CA 19-9 antigen levels in the patient biological samplecan be assayed along with using the monoclonal antibody to CA 19-9 toassay the glycan levels of MUC1, MUC5AC, and MUC 16 in the patientbiological sample. Or, total CA 19-9 antigen levels in the patientbiological sample can be assayed along with using the monoclonalantibody to CA 19-9 to assay the glycan levels of MUC5AC and MUC 16 inthe patient biological sample. Or, total CA 19-9 antigen levels in thepatient biological sample can be assayed along with using the monoclonalantibody to CA 19-9 to assay the glycan level of MUC 16 in the patientbiological sample. Or another glycan binding protein can also be used toassay the glycan level of a mucin in the patient biological sample. Forexample, total CA 19-9 antigen levels in the patient biological samplecan be assayed along with using the monoclonal antibody to CA 19-9 toassay the glycan levels of MUC1, MUC5AC, and MUC 16 in the patientbiological sample, and also using Bauhinea Purpurea lectin (BPL) toassay the glycan level of MUC16 in the patient biological sample. Or,total CA 19-9 antigen levels in the patient biological sample can beassayed along with using the monoclonal antibody to CA 19-9 to assay theglycan levels of MUC5AC and MUC 16 in the patient biological sample, andalso using BPL to assay the glycan level of MUC16 in the patientbiological sample.

The invention provides a method for diagnosing pancreatic cancer; andmore specifically, a method for differentiating pancreatic cancer from abenign pancreatic disease in a subject or patient having or suspected ofhaving a pancreatic disease. This method comprises assaying a patientbiological sample for a total level of CA 19-9 antigen in the patientbiological sample and assaying for a glycan level in a specific mucin(s)in the patient biological sample from the subject or patient, wherein adifferent level of total CA 19-9 antigen in the patient biologicalsample as compared to a statistically validated threshold for total CA19-9 antigen and a different level of glycan level in the specificmucin(s) in the patient biological sample as compared to a statisticallyvalidated threshold for each specific mucin(s) indicates pancreaticcancer in the patient rather than a benign pancreatic disease.

The present invention, in certain aspects, is directed to the diagnosisof pancreatic cancer by comparing the total level of CA 19-9 antigen inthe patient biological sample to a statistically validated threshold fortotal CA 19-9 antigen and by comparing the glycan level in the specificmucin(s) in the patient biological sample to a statistically validatedthreshold for each specific mucin(s). The statistically validatedthreshold for total CA 19-9 antigen is based upon the total level of CA19-9 antigen in comparable samples obtained from a control population,e.g., from patients having a benign pancreatic disease or a pancreaticdisease other than pancreatic cancer. The statistically validatedthreshold for the glycan level in the specific mucin(s) is based uponthe glycan level in each specific mucin(s) in comparable controlbiological samples from a control population, e.g., from patients havinga benign pancreatic disease or a pancreatic disease other thanpancreatic cancer. Various control populations are otherwise describedhereinabove and in the Examples.

The statistically validated thresholds are related to the values used tocharacterize both the total CA 19-9 level and the glycan level in thespecific mucin(s) in the biological sample obtained from the subject orpatient. Thus, if the total CA 19-9 level or the glycan level is anabsolute value, then the control value is also based upon an absolutevalue.

The statistically validated thresholds can take a variety of forms. Forexample, a statistically validated threshold can be a single cut-offvalue, such as a median or mean. Or, a statistically validated thresholdcan be divided equally (or unequally) into groups, such as low, medium,and high groups, the low group being individuals least likely to havepancreatic cancer rather than a benign pancreatic disease and the highgroup being individuals most likely to have pancreatic cancer ratherthan a benign pancreatic disease.

Statistically validated thresholds, e.g., mean levels, median levels, or“cut-off” levels, may be established by assaying a large sample ofindividuals in the select population and using a statistical model suchas the predictive value method for selecting a positivity criterion orreceiver operator characteristic curve that defines optimum specificity(highest true negative rate) and sensitivity (highest true positiverate) as described in Knapp, R. G., and Miller, M. C. (1992). ClinicalEpidemiology and Biostatistics. William and Wilkins, Harual PublishingCo. Malvern, Pa., which is specifically incorporated herein byreference. A “cutoff value may be separately determined for total CA19-9 antigen level and for the glyan level of each specific mucinassayed. Statistically validated thresholds also may be determinedaccording to the methods described in the Examples hereinbelow.

The total CA 19-9 antigen level and the levels of the assayed mucinglycans in the patient biological sample may be compared to singlecontrol values or to ranges of control values. In one embodiment, atotal level of CA 19-9 antigen in a biological sample from a patient(e.g., a patient having or suspected of having a pancreatic disease) ispresent at a higher or lower level (i.e., at a different level) than incomparable control biological samples from patients having a benignpancreatic disease when the total level of CA 19-9 antigen in thepatient biological sample exceeds a threshold of one and one-halfstandard deviations above the mean of the concentration as compared tothe comparable control biological samples. More preferably, a totallevel of CA 19-9 antigen in a biological sample from a patient (e.g., apatient having or suspected of having a pancreatic disease) is presentat a higher or lower level (i.e., at a different level) than incomparable control biological samples from patients having a benignpancreatic disease when the total level of CA 19-9 antigen in thepatient biological sample exceeds a threshold of two standard deviationsabove the mean of the concentration as compared to the comparablecontrol biological samples. Most preferably, a total level of CA 19-9antigen in a biological sample from a patient (e.g., a patient having orsuspected of having a pancreatic disease) is present at a higher orlower level (i.e., at a different level) than in comparable controlbiological samples from patients having a benign pancreatic disease whenthe total level of CA 19-9 antigen in the patient biological sampleexceeds a threshold of three standard deviations above the mean of theconcentration as compared to the comparable control biological samples.

Further, a glycan alteration in a specific mucin in a biological samplefrom a patient (e.g., a patient having or suspected of having apancreatic disease) is present at a higher or lower level (i.e., at adifferent level) than in the specific mucin in comparable controlbiological samples from patients having a benign pancreatic disease whenthe level of glycosylation of the specific mucin in the patientbiological sample exceeds a threshold of one and one-half standarddeviations above the mean of the concentration as compared to thecomparable control biological samples. More preferably, a glycanalteration in a specific mucin in a biological sample from a patient(e.g., a patient having or suspected of having a pancreatic disease) ispresent at a higher or lower level (i.e., at a different level) than inthe specific mucin in comparable control biological samples frompatients having a benign pancreatic disease when the level ofglycosylation of the specific mucin in the patient biological sampleexceeds a threshold of two standard deviations above the mean of theconcentration as compared to the comparable control biological samples.Most preferably, a glycan alteration in a specific mucin in a biologicalsample from a patient (e.g., a patient having or suspected of having apancreatic disease) is present at a higher or lower level (i.e., at adifferent level) than in the specific mucin in comparable controlbiological samples from patients having a benign pancreatic disease whenthe level of glycosylation of the specific mucin in the patientbiological sample exceeds a threshold of three standard deviations abovethe mean of the concentration as compared to the comparable controlbiological samples.

If the total CA 19-9 level and the glycan level in a specific mucin ormucins in the patient biological sample are present at different levelsthan their respective statistically validated thresholds, then thepatient is more likely to have pancreatic cancer rather than a benignpancreatic disease than are individuals with levels comparable to thestatistically validated threshold. The extent of the difference betweenthe subject's levels and statistically validated thresholds is alsouseful for characterizing the extent of the risk and thereby,determining which individuals would most greatly benefit from certainaggressive therapies. In those cases, where the statistically validatedthreshold ranges are divided into a plurality of groups, such asstatistically validated threshold ranges for individuals at high risk,average risk and low risk, the comparison involves determining intowhich group the subject's level of the relevant risk predictor falls.

Another embodiment of the present invention is a diagnostic kit fordifferentiating pancreatic cancer from benign pancreatic diseases. Inone embodiment, a biomarker panel is used to distinguish pancreaticcancer from pancreatitis. The inventive kit for differentiatingpancreatic cancer from a benign pancreatic disease may include anantibody array having (a) a CA 19-9 capture antibody bound thereto and(b) one or more specific anti-mucin capture antibodies bound thereto,which specific anti-mucin capture antibodies may be an anti-MUC1antibody, an anti-MUC5AC antibody, or an anti-MUC16 antibody; or anycombination of these specific anti-mucin antibodies or other anti-mucinspecific antibodies, (c) a detection monoclonal antibody to the CA-19-9antigen; and (d) a container for the detection monoclonal antibody tothe CA-19-9 antigen. The kit also can include a glycan binding proteinother than the detection monoclonal antibody to the CA 19-9 antigen anda container for the glycan binding protein other than the detectionmonoclonal antibody to the CA 19-9 antigen. The glycan binding proteinsother than the detection monoclonal antibody to the CA 19-9 may be oneor more of Aleuria Aurantia lectin (AAL), Wheat Germ Agglutinin (WGA),Jacalin, Bauhinea Purpurea lectin (BPL), Sambucus Nigra lectin (SNA), aglycan-binding antibody, or other glycan binding antibodies describedherein or otherwise known in the art. The kit may be used to perform themethods described herein.

The present diagnostic methods and kits are useful for determining ifand when medical treatments and therapeutic agents that are targeted atslowing or reversing the progression of pancreatic cancer should orshould not be prescribed for an individual patient. Such medicaltreatments and therapeutic agents are known in the art and will beprescribed by a physician based on standard medical practices. In onemethod of the present invention, such medical treatments and therapeuticagents are administered to the patient to treat the pancreatic cancer.

EXAMPLES

The present invention is more particularly described in the followingexamples, which are intended as illustrative only, since modificationsand variations therein will be apparent to those skilled in the art.

Example 1 Serum Samples

Serum samples were assembled from three sites. At Evanston NorthwesternHealthcare (ENH, Evanston, Ill.), serum samples were collected frompatients with pancreatic adenocarcinoma (stage I to stage IV), othergastro-intestinal malignancies (such as colon cancer, duodenal oresophageal cancer), or benign gastro-intestinal diseases (such asampullary adenoma, pancreatitis, cystadenoma, pseudocyst ordiverticulosis). The samples were collected prior to any invasiveprocedures, and the patient diagnoses were confirmed after subsequentprocedures. Control samples also were collected at ENH from high-riskindividuals from pancreatic cancer prone families undergoingsurveillance with Endoscopic Ultrasound (EUS) or Endoscopic RetrogradeCholangiopancreatography (ERCP). The control subjects had no pancreaticlesions, and serum samples were collected using the same procedure asfor the other patients at ENH. Additional samples from pancreatic cancerpatients and healthy control subjects were collected in western Michigan(WM) and at the University of Michigan (UM) hospitals, respectively. Thesamples from WM were collected from seven different hospitals as part ofa phase II clinical trial evaluating combination chemotherapeutic agentsfor the treatment of locally advanced or metastatic pancreaticadenocarcinoma (stage III or higher). Some of the patients had priorsurgical or radiation treatment of the primary tumor, but not withinfour weeks of sample collection, and only after fully recovering fromthe effects of the treatment. No patients had prior systemicchemotherapeutic treatment. The control samples from UM were collectedas part of a liver cancer biomarker study. The subjects were seen bytheir primary care doctor for their routine annual physical and werehealthy with no symptoms of gastro-intestinal disease. The WM sampleswere sent on ice to the Van Andel Research Institute (VARI), where theywere divided into aliquots and frozen at −80° C. within 12 hours ofdrawing. The other samples were stored at each site at −80° C. and weresent frozen on dry ice to the VARI, where they were stored at −80° C.Each aliquot had been thawed no more than two times prior to use. Allsamples were coded to protect patient anonymity and were collected underprotocols approved by local Institutional Review Boards for humansubjects research.

In experiment sets one & two, 32 samples from patients with pancreaticadenocarcinoma, 18 samples from patients with benign gastro-intestinaldiseases, 22 samples from patients with other gastro-intestinalmalignancies, and 33 healthy controls were used. In experiment setsthree & four, 61 samples from patients with pancreatic adenocarcinoma,31 samples from patients with benign diseases, and 50 healthy controlswere used. Seventy-five of the samples were common between sets one &two and sets three & four. Table 1 presents a summary of the sources andassociated demographic information of the samples used in sets three &four. The number of samples in each class, the median ages (standarddeviation in parenthesis), and percent male are shown in Table 6.

TABLE 6 Class Number Age % Male Healthy 50 43.0 (13.0) 33.3 UM 33 39.4(12.1) 21.2 ENH 17 49.4 (11.6) 58.8 Pancreatic cancer 61 66.7 (11.9)50.8 ENH 39 71.4 (9.4)  51.3 WM 22 58.2 (10.7) 50.0 Benign disease 3156.5 (16.8) 50.9

Example 2 Antibodies and ELISA

Antibodies were purchased from various sources. Table 1 shows thecomplete list of antibodies used, and performance characteristics. Theaverage CVs, correlations, and average S/Bs (543 channel only) werecalculated. The “correlation overlap” column refers to the number ofsamples that both gave measurable data in both sets one and two (out of89 possible) or in both sets three and four (out of 138 possible). The“Control S/B/” column gives the average S/B of each antibody in thenegative control experiments, in which unlabeled sera were incubated onthe arrays.

Antibodies that were supplied in ascites fluid, culture supernatant orantisera were purified using Protein A beads (Affi-gel Protein A MAPSkit, Bio-Rad) according to the manufacturer's protocol. The antibodieswere prepared at concentrations of 200-2000 μg/ml (most at 500 μg/ml) in10.1 mM Na2HPO4, 1.8 mM KH2PO4, 137 mM NaCl, 2.7 mM KCl, pH 7.5 (1×PBS)containing 0.02% NaN3. The integrity of each antibody preparation wasexamined by reducing and non-reducing gel electrophoresis. Nine of theantibodies that had been printed did not show bands at the sizesexpected for IgG and were removed from the analyses. Enzyme-linkedimmunosorbent assays (ELISA) were performed using commercially availablekits from Bethyl Corporation (Montgomery, Tex.) for the detection ofhemoglobin and IgM.

Example 3 Fabrication of Antibody Microarray

Microarrays were prepared as previously described by the inventor. Zhou,H., Bouwman, K., Schotanus, M., Verweij, C., Marrero, J. A., Dillon, D.,Costa, J., Lizardi, P. M., and Haab, B. B. Two-color, rolling-circleamplification on antibody microarrays for sensitive, multiplexedserum-protein measurements. Genome Biology, 5: R28, 2004. The antibodysolutions were assembled in polypropylene 384-well microtiter plates (MJResearch), using 20 μl in each well. A piezoelectric non-contact printer(Biochip Arrayer, PerkinElmer Life Sciences) spotted approximately 350μl of each antibody solution on the surfaces of either hydrogel-coated(OptArray slides, Accelr8 Technology Corp.) orultra-thin-nitrocellulose-coated microscope slides (PATH slides, GenTelBiosurfaces). Twelve identical arrays were printed on each slide, witheach array consisting of 88-90 antibodies and control proteins spottedin triplicate in an 18×15 array. The printed hydrogel slides were rinsedthree times in 1×PBS containing 0.5% Tween-20 (PBST0.5), placed in pH8.0 50 mM phosphate buffer with 50 mM glycine to quench unreactedsurface N-hydroxysuccinimide groups, and rinsed three more times inPBST0.5. The slides were assembled in a ProPlate multi-array slidemodule (Grace Bio-Labs) to define separate wells for each array on eachslide. The printed PATH slides were imprinted with a wax border aroundeach of the arrays to define hydrophobic boundaries, using acustom-built device. The slides were rinsed briefly in PBST0.5, blockedfor one hour at room temperature in PBST0.5 containing 1% BSA and 0.3%CHAPS, and rinsed two more times with PBST0.5. Slides were dried bycentrifugation at 150×g for 1 minute prior to sample application.

Example 4 Sample Labeling

The detection strategy was based two-color comparative fluorescence, asshown previously. Miller, J. C., Zhou, H., Kwekel, J., Cavallo, R.,Burke, J., Butler, E. B., Teh, B. S., and Haab, B. B. Antibodymicroarray profiling of human prostate cancer sera: antibody screeningand identification of potential biomarkers. Proteomics, 3: 56-63, 2003;Haab, B. B., Dunham, M. J., and Brown, P. O. Protein microarrays forhighly parallel detection and quantitation of specific proteins andantibodies in complex solutions. Genome Biology, 2: 1-13, 2001. Analiquot from each of the serum samples was labeled withN-hydroxysuccinimide-biotin (NHS-biotin, Pierce), and another aliquotwas labeled with N-hydroxysuccinimide-digoxigenin (NHS-DIG, MolecularProbes). Each 1 μl serum aliquot was diluted with 9 μl of a bufferconsisting of 16.8 mM Na₂HPO₄, 3 mM KH₂PO₄, 230 mM NaCl, 4.5 mM KCl, pH7.5 (1.7×PBS) which contained protease inhibitors (Complete Miniprotease inhibitor cocktail tablet, Roche), at a dilution of 1 tablet in5 ml of buffer. In experiments one & two the buffer also contained 4.4μg/ml BSA labeled with 2,4-dinitrophenol (DNP) and 4.4 μg/ml ofN-terminal FLAG-bacterial alkaline phosphatase fusion protein (Sigma),as normalization controls. The diluted serum was incubated for one houron ice after the addition of 5 μl of 1.5 mM NHS-biotin or NHS-DIG in 15%DMSO. The reactions were quenched by the addition of 5 μl of 1 MTris-HCl, pH 7.5 and incubated on ice for another 20 minutes. Theremaining unreacted dye was removed by passing each sample mix through asize-exclusion chromatography spin column (Bio-Spin P6, Biorad) undercentrifugation at 1000×g for two minutes. The spin columns had beenpre-washed three times with 500 μl of 50 mM Tris, 150 mM NaCl, pH 7.5(1×TBS) containing protease inhibitors. The DIG-labeled samples werecombined to form a reference pool, and equal amounts (typically 15 μl)of the pool were transferred to each of the biotin-labeled samples. Eachsample-reference mixture was brought to a final volume of 40 μl by theaddition of 6 of 1×TBS and 4 μl of 1×TBS containing Super Block (Pierce,prepared according to manufacturer's instructions), 1.0% Brij-35, and1.0% Tween-20.

Example 5 Processing of Antibody Microarrays

Forty microliters of each labeled serum sample mix was incubated on amicroarray with gentle rocking at room temperature for one hour. Theslides were rinsed in 1×PBS with 0.1% Tween-20 (PBST0.1) to remove theunbound sample and subsequently washed three times for three minuteseach in PBST0.1 at ambient temperature with gentle rocking. The slideswere dried by centrifugation at 150×g for 1 minute (nitrocelluloseslides) or by aspiration (hydrogel slides). The biotin- anddigoxigenin-labeled bound proteins were detected by Two-Color,Rolling-Circle Amplification (TC-RCA) as described previously (Zhou, H.,Bouwman, K., Schotanus, M., Verweij, C., Marrero, J. A., Dillon, D.,Costa, J., Lizardi, P. M., and Haab, B. B. Two-color, rolling-circleamplification on antibody microarrays for sensitive, multiplexedserum-protein measurements. Genome Biology, 5: R28, 2004), with minormodifications. The microarrays were incubated for one hour at ambienttemperature with 40 μl of a solution containing 75 nM Circle 1, 75 nMCircle 4.2, 1.0 μg/ml Primer 1-conjugated anti-Biotin, and 1.0 μg/mlPrimer 4.2-conjugated anti-DIG in PBST0.1 with 1 mM EDTA and 5 mg/mlBSA. The microarrays were washed and dried as described above.Microarrays were then incubated with 40 μl of 1× Tango buffer(Fermentas, Hanover, Md.) containing 0.36 units of phi29 DNA polymerase(New England Biolabs), 0.1% Tween-20 and 400 μM dNTPs for 30 minutes at37° C. The microarrays were washed in 2×SSC (300 mM NaCl and 30 mMsodium citrate, dihydrate) with 0.1% Tween-20 (SSCT) as described aboveand dried. Cy3-labeled Decorator 1 and Cy5-labeled Decorator 4.2 wereprepared at 0.1 μM each in SSCT and 0.5 mg/ml herring sperm DNA. Fortymicroliters of this solution was incubated on the microarrays for onehour at 37° C. The microarrays were washed in SSCT and dried asdescribed above. Peak fluorescence emission was detected at 570 nm and670 nm using a microarray scanner (ScanArray Express HT, PerkinElmerLife Sciences).

Example 6 Primary Data Analysis

The inventor used the software program GenePix Pro 5.0 (AxonInstruments) to quantify the image data. An intensity threshold for eachantibody spot was calculated by the formula 3*B*CV_(b), where B is eachspot's median local background, and CV_(b) is the average coefficient ofvariation (standard deviation divided by the average) of all the localbackgrounds on the array. Spots that either did not surpass theintensity threshold in both color channels, had a regression coefficient(calculated between the pixels of the two color channels) of less than0.3, or had more than 50% of the pixels saturated in either colorchannel were excluded from analysis. The ratio of background-subtracted,median sample-specific fluorescence to background-subtracted, medianreference-specific fluorescence was calculated, and the ratios fromreplicate antibody measurements within the same array were averagedusing the geometric mean (log transformed prior to averaging).Normalization was applied to each array. The ratios from each array(averaged over the replicate spots) were multiplied by a normalizationfactor N for each array that was calculated by N═(S_(P)/μ_(P))/A, whereS_(P) is the protein concentration of the serum sample on that array,μ_(P) is the mean protein concentration of all the samples, and A is thearray ratio average for that array. The array ratio average for eacharray was generated by taking the geometric mean of all the antibodyratios on that array. Serum protein concentrations were determined usinga protein assay kit (BCA, Pierce) according to the manufacturersinstructions, and two independent measurements were averaged for eachsample. This normalization method, based on the premise that averageprotein binding to each array is proportional to the total proteinconcentration in the sample, was validated using previously-demonstratedmethods. Hamelinck, D., Zhou, H., Li, L., Verweij, C., Dillon, D., Feng,Z., Costa, J., and Haab, B. B. Optimized normalization for antibodymicroarrays and application to serum-protein profiling. Mol CellProteomics, 2005.

Example 7 Immunoblotting

50 μg of serum protein in 20 μl of 1× non-reducing Laemmli sample bufferwas loaded per lane onto pre-cast polyacrylamide gels (Criterion,Bio-Rad). The percentage acrylamide of the gel varied based on the knownmolecular weight of the protein that was to be probed. The serum sampleswere selected based on the data from the microarray experiments. Foreach protein to be probed, two serum samples were chosen that showedhigh binding to the respective antibody, and two serum samples werechosen that showed low binding to the respective antibody. Followingelectrophoresis, the separated proteins were transferred to 0.2 μmnitrocellulose. The nitrocellulose was rinsed briefly with PBST0.1 andthen blocked overnight at 4° C. in PBST0.1 containing 2% ECL Advanceblocking agent (Amersham). The nitrocellulose was washed two times, 5minutes each, with PBST0.1 and incubated with 10 μg/ml biotinylatedprimary antibody in PBST0.1 containing 2% ECL Advance blocking agentblocking buffer for 60 minutes at ambient temperature while shaking. Theprimary antibodies had been labeled with NHS-biotin (Molecular Probes)according to the manufacturer's instructions. The nitrocellulose waswashed three times, 5 minutes each, with PBST0.1 and incubated for 60minutes with a 1:10⁵ dilution of peroxidase-conjugated streptavidin(Amersham) in blocking buffer. The blot was washed 5 times, 10 minuteseach, with PBST0.1 and developed with the ECL Advance Western BlottingDetection Kit (Amersham) according to the manufacturers instructions.The developed blot was exposed to Hyperfilm (Amersham) for 10-60seconds.

Example 8 Protein Dilution Series Analysis

The following protein standards were used: Purified IgG and IgM fromJackson Immunoresearch; purified complement C3 and cathepsin D andrecombinant C-reactive protein (CRP) from Calbiochem; purifiedhemoglobin from Sigma; purified lipase, plasminogen and carcinoembryonicantigen (CEA) from Fitzgerald Industries; and purifiedalpha-1-antitrypsin from Research Diagnostics. For the proteins CEA,lipase, complement C3 and plasminogen, 5 μl of six different analyteconcentrations were added individually to 10 μl of PBS containing 1 μlof human serum. The serum sample used for each analyte had a lowendogenous level of that analyte, based on results from the antibodymicroarray profiling. Each dilution was labeled with biotin as describedabove, and another aliquot of each serum sample, without any analytesspiked in, was labeled with digoxigenin as a reference. Eachbiotin-labeled solution was mixed with an equal amount ofdigoxigenin-labeled reference, and the mixtures were analyzed onantibody microarrays using TC-RCA detection. The final analyteconcentrations were 0, 0.2, 1, 5, 25 and 125 μg/ml, and the final serumdilutions were 1:40. Alternatively, purified proteins were spiked into aBSA background. Dilution series of complement C3, plasminogen,alpha-1-antitrypsin, cathepsin D, CRP, hemoglobin, IgG and IgM werespiked into 6 mg/ml bovine serum albumin (BSA) and labeled with biotinas described above. Other aliquots of each were labeled withdigoxigenin, and the digoxigenin-labeled solutions from each analytewere pooled as a reference and mixed with the biotin-labeled solutionsof that analyte. Six different analyte concentrations of complement C3and plasminogen (0, 0.2, 1, 5, 25 and 125 μg/ml), eight differentanalyte concentrations of CRP, hemoglobin, IgG and IgM (0, 0.0008,0.005, 0.028, 0.17, 1, 6 and 36 μg/ml), four different analyteconcentrations of alpha-1-antitrypsin (0.1, 1, 10 and 100 μg/ml), andthree different analyte concentrations of cathepsin D (0.1, 1 and 10μg/ml) were incubated on the arrays and detected with TC-RCA or indirectdetection. Zhou, H., Bouwman, K., Schotanus, M., Verweij, C., Marrero,J. A., Dillon, D., Costa, J., Lizardi, P. M., and Haab, B. B. Two-color,rolling-circle amplification on antibody microarrays for sensitive,multiplexed serum-protein measurements. Genome Biology, 5: R28, 2004).The digoxigenin-labeled analyte reference concentrations were theaverages of the concentrations in each dilution series (since thesolutions in a series were pooled to form the reference), and the finalBSA concentration was 1.5 mg/ml in each color.

Example 9 Multiparametric Classification

The boosting decision tree method is a modification of the AdaBoostprocedure (Freund, Y. and Schapire, R. A decision-theoreticalgeneralization of on-line learning and an application to boosting. JComputer & Syst Sci, 55: 119-139, 1997), and the real boosting method(Yasui, Y., Pepe, M., Thompson, M. L., Adam, B. L., Wright, G. L., Jr.,Qu, Y., Potter, J. D., Winget, M., Thornquist, M., and Feng, Z. Adata-analytic strategy for protein biomarker discovery: profiling ofhigh-dimensional proteomic data for cancer detection. Biostatistics, 4:449-463, 2003) was developed to handle high-dimensional proteomic data.At each iteration, both boosting procedures update and assign weights toevery sample in the classification based on the accuracy of the currentselected classifier. The samples which are misclassified gain moreweight in the next iteration. Therefore, the next classifier focuses onthe samples misclassified by the previous classifier. In logisticregression with forward selection, samples are equally weighted, and ateach iteration, the best classifier which has the lowest p-value amongthe remaining antibodies is selected into the linear combination of thepreviously selected classifiers. The coefficients of the classifiers areupdated correspondingly. The inventor used a cross-validation process todetermine the optimal number of antibodies in the final combinedclassifier for all three methods. For the boosting methods, a 10-foldcross-validation step is applied, where 90% of the samples are used astraining set to define a best model for classification while 10% of thesamples are reserved as testing set to determine the error rate of themodel. This process is repeated ten times, each time using a differentgroup of 90% for classification, and the cross-validation error is theaverage of the ten error rates. The classifier is considered final whenthe further addition of an antibody will not further decrease thecross-validation error. For logistic regression with forward selection,a 3-fold cross-validation step is applied similar to above. Although theempirical evidence supports the idea that the boosting may avoidover-fitting (Friedman, J. H., Hastie, T., and Tibshirani, R. AdditiveLogistice Regression: a Statistical View of Boosting. Annals ofStatistics, 28: 337-407, 2000), there are counter examples, and notheoretical guidance exists as to when over-fitting may occur. Thecross-validation process simulates the uncertainty in the classificationalgorithm and estimates the prediction error of the selected combinedclassifier. Therefore, this validation gives extra protection againstthe chance of over-fitting, or creating a classifier specifically for aparticular sample set.

Example 10 Total DCP and Glycosylation Assays

The above-described antibody microarray method was used to measure bothdes-carboxy prothrombin (DCP) level and the level of glycosylated DCP in23 serum samples from pancreatic cancer patients and 23 samples fromhealthy controls. An anti-DCP antibody was immobilized on a coated glassmicroscope slide, the proteins from each serum sample were labeled withdigoxigenin and incubated on a glass slide. After washing off unboundprotein, the digoxigenin label was detected by incubation withCy5-labeled anti-digoxigenin and scanning for fluorescence at 635 nm (todetect Cy5). The Cy5 fluorescence reflects the total level of DCP in theserum, and the levels from each of the samples are shown by the line inthe graphs (FIGS. 6 and 7). The levels in the cancer patients aresignificantly higher than the levels in the healthy subjects.

In addition, the inventor incubated a biotin-labeled lectin (SambucusNigra Lectin (SNA)) on the arrays after the serum samples had beenincubated. SNA binds to sialic-acid-containing glycans on the proteins.The biotin was detected with streptavidin-phycoerythrin (SA-PE) andscanning at 532 nm. (The SA binds the biotin, and the PE fluoresces at532 nm.) This signal reflects the amount of sialic-acid-containingglycans on the proteins that were captured by anti-DCP. This level isshown by the line (FIGS. 6 and 7). These levels are generally greater inthe cancer patients than in the healthy controls, and the differencebetween the groups is greater than when measuring total protein. Thatobservation means that the level of sialic acid on the DCP from cancerpatients is greater than the level of sialic acid on normal DCP. The useof this glycosylation measurement could enhance the diagnosticperformance of DCP.

Example 11 Detecting Glycans on Multiple Specific Proteins

Unlabeled serum samples are incubated on antibody microarrays, and afterwashing away unbound serum proteins, the glycan levels on the capturedproteins are detected with a biotin-labeled lectin (FIG. 11A). A secondversion (FIG. 11B) is similar, except that the serum proteins arelabeled with digoxigenin prior to application to an array, and thebiotinylated lectin and digoxigenin-labeled proteins are detected withdifferent color fluorescent dyes so that both protein levels and glycanlevels can be measured in a single experiment. The detection of glycanson proteins captured by immobilized antibodies was confirmed afterincubation of either buffer, unlabeled serum, or labeled serum onantibody arrays. The arrays were then incubated with biotinylated SNAlectin, followed by detection with Cy5-labeled anti-digoxigenin (red,633 nm) and Cy3-labeled anti-biotin (green, 543 nm). The arrays werescanned at both 543 nm and 633 nm, and fluorescence from the spots areshown in both color channels from the indicated antibodies (FIG. 11C).

When no serum sample was incubated, very little binding from the SNAlectin or the anti-digoxigenin was detected at certain antibodies (FIG.11C), although strong binding of certain lectins is observed on someother antibodies (not shown), meaning that the lectin bound to glycangroups on the capture antibodies.

To prevent the GBPs from binding glycan groups on the spottedantibodies, the inventor developed a method to block such binding.Specifically, the method employs oxidation of the carbohydrate groups onthe spotted antibodies, followed by derivatization with ahydrazide-maleimide bifunctional crosslinking reagent, followed byattachment of a cysteine-glycine dipeptide to the maleimide group (FIG.12A). The inventor tested the ability of this method to block thebinding of the lectin Aleuria Aurantia (AAL) to antibody arrays. Thebiotinylated AAL (from Vector Laboratories, Catalog No. B-1395) used bythe inventor is isolated from Aleuria aurantia mushrooms. This lectin isa dimer of two identical subunits of about 36,000 daltons each with anisoelectric point of about pH 9. Aleuria aurantia lectin bindspreferentially to fucose linked (α-1,6) to N-acetylglucosamine or tofucose linked (α-1,3) to N-acetyllactosamine related structures. The AALused by the inventor bound strongly to many antibodies on arrays thathad not been blocked (FIG. 12C, top left), with some addition binding tocaptured proteins after incubation of serum (FIG. 12C, top right). Afterblocking, very little AAL bound to the capture antibodies (FIG. 12C,bottom left), yet the blocked arrays still showed binding of the lectinafter serum incubation (FIG. 12C, bottom right), indicating that theblocking procedure had not eliminated the antibody binding activities.The fluorescence intensity from incubated streptavidin-phycoerythrin isshown. The ratios of the AAL binding after serum incubation to the AALbinding after just buffer incubation showed that the blocking proceduresignificantly enhanced the detection of glycans on captured proteinsrelative to the undesired binding to the antibodies (FIG. 12D).

Further establishing the effectiveness of the blocking method, theinventor chemically blocked arrays and incubated either with a buffersolution (top arrays) or blood serum (bottom arrays). The arrays weredetected with the indicated lectins or the CA19-9 antibody (which bindsa carbohydrate group) (FIG. 18). The spots lighting up in the top panels(FIG. 18) are positive controls and spotted lectins, which may bind thedetection lectin. Binding of the detection lectins to the spottedantibodies is very weak. When serum is incubated, more signal at eachantibody is detected FIG. 18, bottom panels), showing that the lectinsare detecting captured proteins, not the spotted antibodies. The GBPsused in this work were SNA (Sambucus Nigra), PNA, WGA (Wheat GermAgglutinin), Jacalin, and anti-CA19-9 antibody (FIG. 18).

The binding of the lectin to glycan groups, rather than non-specificbinding to the core protein, was confirmed through competitive bindingwith free sugars. The lectin AAL was pre-incubated with either L-fucoseor sucrose (FIG. 13A), and the lectin Wheat Germ Agglutinin (WGA) waspre-incubated with either diacetyl chitobiose or sucrose (FIG. 13B),prior to application to arrays. The inventor also used biotinylated,succinylated WGA (from Vector Laboratories, Catalog No. B-1025S)isolated from Triticum vulgaris (wheat germ). This derivative hasproperties distinct from the native lectin. Evidence suggests thatsuccinylated wheat germ agglutinin does not bind to sialic acidresidues, unlike the native form, but retains its specificity towardN-acetylglucosamine (Eur. J. Biochem. 98, 39, 1979 and Eur. J. Biochem.104, 147, 1980). Using conjugates of the native lectin and thesuccinylated form can provide a system to distinguish between sialylatedglycoconjugates and those containing only N-acetylglucosaminestructures.

Each lectin preparation was incubated on an antibody array that had beenincubated with a serum sample (all arrays had the same sample). Thelectin concentration was held constant at 10 μg/ml. The fluorescenceintensity from streptavidin-phycorerythrin at the indicated antibodiesis shown (FIG. 13). In each case, lectin binding to captured serumproteins was reduced by the L-fucose or diacetyl chitobiose, but not bythe sucrose, indicating the lectins were binding to the capturedproteins according to their particular glycan specificities. Theantibodies shown here were of interest for associations with cancer (seebelow) and are representative of all antibodies on the arrays.

Example 12 Measuring Variation in Glycans on Serum Protein

The inventor tested the ability to measure changes in glycan levelsacross different serum samples using the method of FIG. 11B, whichallows the detection of both protein level and glycan level in the sameexperiment. These initial tests were performed on arrays that were notchemically blocked, focusing on antibodies that showed low lectinbinding and assessing the ability to detect glycan variation on top ofnon-specific binding to the antibodies. Twenty-three serum samples frompancreatic cancer patients and 23 serum samples from healthy controlswere labeled with the digoxigenin tag, incubated on antibodymicroarrays, and detected with the lectin indicated (Table 7) andCy-5-labeled antidigoxigenin. Six sets of the 46 samples were run, andeach set was incubated with a different biotinylated lectin, using thelectins SNA (in duplicate), AAL (in duplicate), Maackia Amurensis (MAA),and wheat germ agglutinin (WGA).

T-tests were performed on data from the individual color channels and onthe ratio between the two. Few glycan differences were revealed usingthe SNA and MAA lectins, but many differences were revealed using theAAL and WGA lectins (Table 7).

TABLE 7 Detected with AAL Name p-value: set 1, set 2 Net 633Anti-PIVKA-II  0.002, <0.001 Anti-von Willebrand Factor  0.02, <0.001Net 543 Anti-Alpha 2-macroglobulin <0.001, <0.001 Anti-Hamoglobin<0.001, <0.001 Anti-Alpha-1-antitrypsin  0.001, <0.001 Anti-vonWillebrand Factor  0.002, <0.001 Anti-Haptoglobin beta subunit  0.009,<0.001 anti-Haptoglobin  0.01, 0.002 Lectin, Wheat germ agglutinin 0.01, <0.001 Lectin, Jacalin 0.008, 0.004 Lectin, Erythrina Cristagalli 0.01, 0.003 Ratio 543/633 Anti-Alpha-1-antitrypsin <0.001, <0.001anti-Haptoglobin <0.001, <0.001 Anti-Alpha 2-macroglobulin <0.001,<0.001 Anti-Hemoglobin <0.001, 0.003  Anti-Albumin <0.001, 0.01 Anti-PIVKA-II  0.02, 0.003 Lectin, Wheat germ agglutinin 0.02, 0.02Anti-Haptoglobin beta subunit  0.04, 0.005 Detected with MAA Namep-value Net 633 Anti-Von Willebrand Factor <0.001 Anti-PIVKA-II 0.001Lectin, Wheat Germ Agglutinin (WGA) 0.002 Anti-CRP 0.05 Net 543 Lectin,Wheat Germ Agglutinin (WGA) 0.001 Lectin, Aleuria Aurantia 0.01 Anti-IgA0.02 IgG, Rabbit 0.02 Lectin, Dolichos Biflorus Agglutinin (DBA) 0.04Ratio 543/633 Anti-Von Willebrand Factor <0.001 Lectin, Wheat GermAgglutinin (WGA) <0.001 Anti-PIVKA-II 0.03 Detected with WGA Namep-value Net 633 Anti-Von Willebrand Factor <0.001 Net 543 Anti-VonWillebrand Factor <0.001 Lectin, Wheat Germ Agglutinin (WGA) <0.001Lectin, Erythrina Cristigalli <0.001 Lectin, Bauhinia Purpurea (BPL)<0.001 Anti-CRP <0.001 Anti-GLA <0.001 Anti-Sialyl Lewis A <0.001Anti-MUC1 (CA15-3) 0.001 Anti-IgA 0.006 Lectin, Vicia Villosa (VVL)0.006 Lectin, Aleuria Aurantia 0.008 Anti-CA 19-9 0.011 Anti-CEA 0.011Anti-Plasminogen 0.022 anti-Haptoglobin 0.027 anti-Haptoglobin b subunit0.029 Lectin, Lotus Tetragonolobus (LTL) 0.044 Anti-Blood group Lewis A0.048 Ratio 543/633 Anti-CA 19-9 <0.001 Anti-Blood group Lewis A <0.001Lectin, Wheat Germ Agglutinin (WGA) <0.001 Lectin, Bauhinia Purpurea(BPL) 0.02 Lectin, Aleuria Aurantia 0.03 Anti-Lipase 0.048 Detected withSNA Name p-value: set 1, set 2 Net 633 Anti-Von Willebrand Factor<0.001, <0.001 Anti-PIVKA-II  0.003, <0.001 Anti-CRP 0.02, 0.03 Net 543Anti-PIVKA-II <0.001, <0.001 Anti-Von Willebrand Factor  0.001, <0.001Ratio 543/633 None

P-values are shown just for the significant antibodies (p<0.05). For theAAL and SNA data, p-values are given for those that were significant inboth of the replicates.

The antibodies and lectins were purchased from various sources. Theantibodies used are summarized in Table 8. The antibodies were purifiedand prepared as described earlier (Orchekowski, R. P. et al. Antibodymicroarray profiling reveals individual and combined serum proteinsassociated with pancreatic cancer. Cancer Research, in press (2005)).

TABLE 8 Name Used in Set Origin Clonality Source/Manufacturer CatalogNumber Anti-Von Willebrand Factor 1, 2, 3 Rabbit Polyclonal DAKO A0082Anti-α-1-antitrypsin 1, 2, 3 Mouse Monoclonal Biotrend 0640-5507Anti-Haptoglobin (ab #1) 1, 2, 3 Sheep Polyclonal Biotrend 4890-0004Anti-MUC1 (CA15-3) (Ab #1) 1, 2, 3 Mouse Monoclonal USBio C0050-23Anti-Carcinoembryonic Antigen (CEA) 1, 2, 3 Mouse Monoclonal USBioC1299-94 Anti-Ceruloplasmin 1, 2, 3 Goat Polyclonal Sigma C0912Anti-C-reactve Protein (CRP) 1, 2, 3 Mouse Monoclonal USBio C7907-10Anti-Blood Group Lewis a 1, 2, 3 Mouse Monoclonal AbCam ab3967Anti-Sialyl Lewis a 1, 2, 3 Mouse Monoclonal AbCam ab3982Anti-Des-Carboxy Prothrombin (DCP) 1, 2, 3 Mouse Monoclonal USBioP4210-50 Sambusu Nigra Lectin (SNA) 1, 2, 3 N/A N/A Calbiochem 431792Ulex Europaeus Agglutinin (UEA I) 1, 2, 3 N/A N/A Vector LaboratoriesL-1060 Jacalin Lectin 1, 2, 3 N/A N/A Vector Laboratories L-1150 WheatGerm Agglutinin (WGA) 1, 2, 3 N/A N/A Vector Laboratories L-1020Anti-Haptoglobin β-subunit 1, 2, 3 Mouse Monoclonal Sigma H 6395Anti-Haptoglobin (Ab #2) 1, 2, 3 Rabbit Polyclonal Sigma H 8636Anti-O-linked N-Acetyl glucosamine 1, 2, 3 Mouse Monoclonal AbCam ab2739Anti-IgG-Fc 1, 2, 3 Goat Polyclonal Bethyl A80-104A Anti-Hemoglobin 1,2, 3 Sheep Polyclonal Bethyl E80-135 (kit) Anti-Transferrin 1, 2, 3 GoatPolyclonal Bethyl E80-128 (kit) Anti-α 2-macroglobulin 1, 2 RabbitPolyclonal Sigma M1893 IgG, Goat 1, 2 Goat N/A Jackson ImmunoResearch005-000-003 IgG, Rabbit 1, 2 Rabbit N/A Jackson ImmunoResearch011-000-003 Anti-CA19-9 (Ab #2) 1, 3 Mouse Monoclonal USBio C0075-27Anti-Gla 1, 3 Mouse Monoclonal American Diagnostica, Inc. 3570 AleuriaAurantia Lectin (AAL) 1, 3 N/A N/A Vector Laboratories L-1390 BauhiniaPurpurea Lectin (BPL) 1, 3 N/A N/A Vector Laboratories L-1280 ErythrinaCristigalli Lectin (ECL) 1, 3 N/A N/A Vector Laboratories L-1140 Lectin,Vicia Villosa (VVL) 1, 3 N/A N/A Vector Laboratories L-1230 Anti-IgA 1,3 Goat Polyclonal Bethyl E80-102 (kit) Anti-α-fetoprotein (Ab #1) 2, 3Mouse Monoclonal Biotrend 4F16 anti-Thrombospondin-1 2, 3 MouseMonoclonal Lab Vision MS-590-PABX Ricinus communis Agglutinin I (RCA120) 2, 3 N/A N/A Vector Laboratories L-1080 anti-Apolipoprotein E (ApoE) 2, 3 Mouse Monoclonal Chemicon International, Inc. MAB1062Anti-Clusterin (apolipoprotein J, Apo J) 2, 3 Mouse Monoclonal Alexis(Axxora) ALX-804-126-C100 Anti-IgM 1 Goat Polyclonal JacksonImmunoresearch 109-005-043 Anti-alkaline phosphatase (AP) 1 MouseMonoclonal Biotrend 0300-0430 Anti-beta lipoprotein (LDL) 1 RabbitPolyclonal Biotrend 5685-3010 Anti-MUC1 (CA 15-3) (Ab #2) 1 MouseMonoclonal Ilan/Jim Resau (VAI) Unknown Anti-Caveolin-1 1 RabbitPolyclonal Sigma C3237 Anti-Lipase 1 Mouse Monoclonal USBio L2496-02Anti-Hemoglobin, glycated 1 Mouse Monoclonal USBio H1850-21 DolichosBiflorus Agglutinin (DBA) 1 N/A N/A Vector Laboratories L-1030 LotusTetragonolobus Lectin (LTL) 1 N/A N/A Vector Laboratories L-1320Anti-Albumin 1 Mouse Monoclonal Fitzgerald 10-A75 Anti-Fibronectin 2Rabbit Polyclonal Sigma F 3648 Anti-Plasminogen 2 Mouse Monoclonal USBioP4256-27A Anti-Cathepsin B 2 Goat Polyclonal R&D Systems AF953 IgG,Mouse 2 Mouse N/A Jackson ImmunoResearch 015-000-003 IgG, Sheep 2 SheepN/A Jackson ImmunoResearch 013-000-003 Anti-Blood Group Lewis x 2 MouseMonoclonal AbCam ab3358 anti-Kininogen 2 Goat Polyclonal R & D SystemsAF1569 Anti-IL-8 3 Goat Polyclonal R & D Systems AB-208-NA Anti-Laminin3 Mouse Monoclonal Sigma L 8271 Anti-CA125 3 Mouse Monoclonal USBioC0050-01 Anti-CA19-9 (Ab #1) 3 Mouse Monoclonal USBio C0075-07Anti-α-fetoprotein 3 Mouse Monoclonal Sigma A8452 Anti-Cathepsin D 3Goat Polyclonal R&D Systems AF1014 anti-Protein S 3 Sheep PolyclonalUSBio P9108-09 Peanut Agglutinin (PNA) 3 N/A N/A Vector LaboratoriesL-1070 anti-90K/Mac-2BP 3 Mouse Monoclonal Bender MedSystems BMS146

Several different behaviors of glycan and total protein variation wereobserved after detection with the AAL lectin (FIG. 14A). For eachindicated antibody, the distributions of red fluorescence (indicatingtotal protein binding), green fluorescence (indicating AAL lectinbinding), and ratio of green-to-red (indicating amount of AAL bindingrelative to protein level) were found, using the average results fromtwo replicate experiment sets. Results from both the AAL and SNA lectinsare shown for des-carboxy prothrombin. The antibody toalpha-2-macroglobulin had similar captured protein levels (red signal)between the groups, but the lectin binding (green signal), and the ratioof lectin binding to captured protein (green:red ratio), weresignificantly lower in cancer. In contrast, the antibodies targetinghaptoglobin and hemoglobin (which can be complexed together) showedsimilar captured protein levels but higher lectin binding in cancer. Thevon Willebrand factor antibody showed elevated lectin binding in cancerbut also elevated captured protein, so the glycan-to-protein ratio didnot change between the groups. The protein des-carboxy prothrombin (DCP)showed unique lectin binding patterns: both SNA and AAL binding werealtered in cancer, but in different directions. The lectin SNA, whichbinds sialic acid, showed increased glycan:protein ratios in cancer, butthe AAL lectin, which targets fucose, showed lower glycan:protein ratiosin cancer. The final example (lower right in FIG. 14A) shows the use ofa lectin as the capture agent, showing that total glycoprotein levelsdefined by the lectins WGA and AAL are elevated in pancreatic cancer.

FIG. 14B shows Western blot studies used to confirm the trends inglycosylation alterations as measured by microarray. The relative levelsof haptoglobin and alpha-2-macroglobulin were probed in 15 and 16samples by immunoblot, respectively, and the same bands were againprobed using the lectin AAL after stripping the blot (FIG. 14B, top).The intensities of the bands were quantified to obtain the distributionsof protein levels (band intensities from the anti-haptoglobin oranti-alpha-2-macroglobulin blots), glycan levels (band intensities fromthe AAL blots), and the glycan-to-protein levels (ratios of the previoustwo band intensities) in the cancer (dark bars) and healthy controlpatient (white bars) samples (FIG. 14B, bottom). The boxes give theupper and lower quartiles of the measurements with respect to the medianvalue (horizontal line in each box), and the lines give the ranges ofthe measurements, excluding outliers, which are represented by circles.The asterisk indicates a significant difference (p<0.05) between thegroups. The distributions followed the same trends as observed in themicroarray data; the level of AAL binding was increased on haptoglobinand decreased on alpha-2-macroglobulin in cancer, relative to therespective protein levels.

Example 13 Profiling Glycolsylation Alterations on Serum Proteins inPancreatic Cancer

The inventor investigated the associations of multiple glycan andprotein levels with cancer using a larger set of serum samples (Table 9)on chemically-blocked arrays. As discussed herein, the profiling ofglycan variation, using the lectins AAL and WGA, over serum samples frompancreatic cancer patients and control subjects showed multiple proteinswith potential cancer-associated glycan elevations or reductions. Thepatterns of reactivity from the two lectins were very different fromeach other, indicating distinct mechanisms of regulation for thoseglycans. Parallel glycan-detection and sandwich assays showed thatglycans reactive with the CA 19-9 monoclonal antibody were elevated onthe proteins carcinoembryonic antigen and MUC1 in sera from pancreaticcancer patients, which could influence the binding and functionalproperties of those proteins. As demonstrated by these data, antibodyarrays with glycan detection are highly effective for profilingvariation in specific glycans on multiple proteins and should be usefulin diverse areas of glycobiology research.

TABLE 9 Class Number Age % Male Healthy Source 110 46.9 (13.4) 35.5 ENH33 49.4 (11.9) 54.5 UM 77 45.9 (14.0) 27.2 Pancreatic Cancer 119 68.1(11.4) 49.6 ENH 89 70.0 (11.4) 51.7 UM 30 62.4 (9.9)  50.0 BenignDisease 104 57.0 (15.0) 49.0 ENH 89 58.3 (14.9) 51.7 UM 15 49.3 (13.6)33.3ENH: Evanston Northwestern Healthcare; UM: University of Michigan. Themean and % male are given, with the standard deviation of the ages inparentheses.

The one-color method (FIG. 11A) was chosen for these experiments toeliminate the possibility of interaction between the lectin and theanti-digoxigenin. The sample set was run three times: twice detectedwith the lectin AAL and once with the lectin WGA.

Many antibodies showed significantly different lectin binding levels(p<0.05) between the patient groups using both the AAL and WGA lectins.Table 10 describes antibodies showing discrimination between patientclasses, with p-values.

TABLE 10 AAL WGA Cancer vs Cancer vs Benign vs Cancer vs Cancer vsBenign vs Antibodies/Lectins Healthy Benign Healthy Antibodies/LectinsHealthy Benign Healthy Anti-Haptoglobin β-subunit 3.7E−05 4.4E−03 >0.05Anti-CA 19-9 (Ab#1) 4.3E−11 7.2E−09 >0.05 Anti-O-linked N-acetylglucosamine 8.2E−04 0.037 >0.05 Anti-CA 19-9(Ab#2) 3.5E−06 1.8E−04 >0.05Anti-Blood Group Lewis x 1.6E−03 2.5E−03 >0.05 Anti-Blood group Lewis a2.2E−05 1.1E−03 >0.05 Sambucus Nigra Lectin 2.6E−07 >0.05 2.0E−04Anti-α-fetoprotein (Ab#2) 1.5E−04 0.032 >0.05 Anti-Apolipoprotein E7.4E−05 >0.05 0.017 Ulex Europaeus Agglutinin I 4.5E−04 7.2E−03 >0.05Jacalin Lectin 1.7E−04 >0.05 4.6E−03 Anti-O-linked N-acetyl glucosamine8.0E−03 4.0E−03 >0.05 Ulex Europaeus Agglutinin 1 1.1E−06 0.02 0.014Anti-Protein S 1.1E−03 0.037 >0.05 Anti-Von Willebrand Factor 3.6E−090.046 2.6E−04 Anti-Sialyl Lewis a 8.9E−03 0.043 >0.05 Anti-Haptoglobin(Ab#2) 3.1E−15 4.2E−5  5.7E−6  Anti-GLA 0.011 0.028 >0.05 Anti-Clusterin9.6E−15 3.9E−05 1.0E−04 Anti-90K 0.040 0.030 >0.05 Anti-Kininogen4.6E−11 7.4E−04 1.9E−03 Anti-Laminin 4.6E−06 >0.05 0.017Anti-Ceruloplasmin 4.8E−11 2.7E−04 7.2E−03 Anti-IL-8 1.8E−05 >0.054.9E−03 Ricinus Communis Agglutinin I 8.5E−10 0.009 7.5E−04Anti-Des-Carboxy Prothrombin 2.9E−05 >0.05 0.021 Anti-C-reactive Protein1.2E−09 0.009 3.46E−05  Anti-C-reactive Protein 3.5E−05 >0.05 1.5E−03Ant-α-fetoprotein (Ab#1) 7.5E−09 0.004 5.7E−03 Anti-CarcinoembryonicAntigen 5.0E−05 >0.05 0.022 Anti-α-1-antitrypsin 3.6E−07 0.019 6.9E−03Erythrina Cristagalli Lectin 0.041 >0.05 0.021 Anti-Sialyl Lewis a1.8E−06 0.046 4.3E−03 Vicia Villosa Lectin 5.3E−06 0.013 0.045 WheatGerm Agglutinin 2.2E−06 0.009 0.026 Anti-Von Willebrand Factor 7.8E−060.039 0.011 Anti-α-2-macroglobullne 0.026 >0.05 >0.05 Anti-Haptoglobin(Ab#1) 8.0E−06 0.049 0.015 Anti-Plasminogen 6.0E−04 >0.05 >0.05Anti-CA125 1.2E−05 0.035 6.4E−03 Anti-MUCI (CAI 5-3)(Ab#2)5.4E−03 >0.05 >0.05 Anti-MUC1 (CA15-3)(Ab#2) 6.1E−04 >0.05 >0.05Anti-Carcinoembryonic Antigen 9.9E−03 >0.05 >0.05 Anti-α-1-antitrypsin6.3E−04 >0.05 >0.05 Anti-Fibronectin 0.016 >0.05 >0.05 Wheat GermAgglutinin 4.3E−03 >0.05 >0.05 Anti-α-fetoprotein (Ab#1)5.6E−03 >0.05 >0.05 Anti-Cathepsin D 6.2E−03 >0.05 >0.05Anti-Transferrin 8.9E−03 >0.05 >0.05 Bauhinia Purpurea Lectin0.018 >0.05 >0.05 Anti-Ceruloplasmin 0.021 >0.05 >0.05Anti-Clusterin >0.05 0.032 >0.05 Aleuria Aurantia Lectin >0.050.033 >0.05 Peanut Agglutinin >0.05 0.043 >0.05 JacalinLectin >0.05 >0.05 0.037All of the differences were higher in cancer relative to healthy andbenign disease and higher in benign disease relative to healthy, exceptfor alpha-2-macroglobulin, which was lower in cancer when detected withAAL, consistent with the data of FIG. 14. The agreement between thesedata, collected using chemically-blocked antibodies, and the datacollected with unmodified antibodies (FIG. 4 and Table 8) providedfurther indication that the blocking procedure did not affect thebinding activities of the antibodies. The duplicate AAL sets showed thatthe data are reproducibile. The correlations between the duplicateexperiment sets ranged from 0.36 to 0.76 for the antibodies of Table 9,with the best discriminators above 0.70, which is good correlation over333 samples. In addition, of the 23 antibodies that discriminated thepatient groups in AAL set two (presented in Table 10), 21 alsodiscriminated the patient groups in set one.

The unmatched age distributions between the different groups could be asource of bias in the comparisons. The inventor tested that source ofbias by comparing the glycan levels between the older third and youngerthird of subjects within each patient group. Detection with WGA revealedonly one antibody with different levels (p<0.05) between any of thegroups (von Willebrand factor higher in older subjects with benigndisease), and detection with AAL showed no reproducible (p<0.05 in bothsets one and two) differences between the groups. This analysisindicates that age differences are likely not major contributors to theobserved differences between the patient groups.

The inventor investigated whether any of the antibody-lectin pairs, orcombinations of pairs, could possibly be used as a biomarker forpancreatic cancer. The highest discrimination between cancer and theother classes was given by anti-CA19-9 detected with WGA. Thearea-under-the-curve (AUC) in receiver-operator characteristic analysiswas 0.92 (81% sensitivity and 90% specificity) in the comparison of thecancer to healthy classes, and 0.86 (77% sensitivity and 81%specificity) in the comparison of the cancer and benign classes. Thenext best discriminators were CA125 detected with WGA (0.78 and 0.65 AUCcomparing cancer to healthy and cancer to benign, respectively) andhaptoglobin detected with AAL (0.81 and 0.66 AUC comparing cancer tohealthy and cancer to benign, respectively). Since CA19-9 detected withWGA was much stronger than the other markers, the use of logisticregression and boosting algorithms to classify the samples based oncombinations of multiple markers provided no statistical improvement inclassification accuracy over the use of that marker alone, which hassimilar performance to the standard CA19-9 assay.

The inventor compared profiles between the data sets to gain insightsinto the coordinated regulation of the WGA-reactive and AAL-reactiveglycan structures. Some antibodies strongly discriminated the patientgroups with one lectin but not the other, which indicatedcancer-related, differential regulation of the structures. Table 11describes antibodies used in both AAL and WGA detection experiments,with p-values for each class comparison.

TABLE 11 AAL WGA Cancer vs Cancer vs Benign vs Cancer vs Cancer vsBenign vs Antibodies/Lectins Healthy Benign Healthy Healthy BenignHealthy Anti-Haptoglobin β-subunit 3.7E−05 4.4E−03 0.175 0.995 0.6450.631 Anti-O-linked N-acetyl glucosamine 8.2E−04 0.037 0.099 8.0E−034.0E−03 0.913 Anti-Von Willebrand Factor 3.6E−09 0.046 2.6E−04 7.8E−060.039 0.011 Sambucus Nigra Latin 2.6E−07 0.140 2.0E−04 0.092 0.422 0.410Ulex Europaeus Agglutinin 1 1.1E−06 0.020 0.014 4.5E−04 7.2E−03 0.344Anti-Apolipoprotein E 7.4E−05 0.31 0.017 0.156 0.363 0.310 JacalinLectin 1.7E−04 0.726 4.6E−03 0.750 0.088 0.037 Anti-Haptoglobin (Ab#2)3.1E−15 4.2E−5  5.7E−6  0.092 0.095 0.936 Anti-Clusterin 9.6E−15 3.9E−051.0E−04 0.421 0.032 0.323 Anti-Ceruloplasmin 4.8E−11 2.7E−04 7.2E−030.021 0.466 0.095 Ricinus Communis Agglutinin I 8.5E−10 0.009 7.5E−040.552 0.820 0.614 Anti-C-reactive Protein 1.2E−09 0.009 3.46E−05 3.5E−05 0.210 1.5E−03 Ant α-fetoprotein (Ab#1) 7.5E−09 0.004 5.7E−035.6E−03 0.065 0.588 Anti-α-1-antitrypsin 3.6E−07 0.019 6.9E−03 6.3E−040.058 0.104 Anti-Sialyl Lewis a 1.8E−06 0.046 4.3E−03 8.9E−03 0.0430.464 Wheat Germ Agglutinin 2.2E−06 0.009 0.026 4.3E−03 0.249 0.084Anti-MUC1 (CA15-3)(Ab#2) 5.4E−03 0.192 0.243 6.1E−04 0.140 0.076Anti-Carcinoembryonic Antigen 9.9E−03 0.256 0.177 5.0E−05 0.212 0.022Anti-Haptoglobin(Ab#1) 0.272 0.290 0.981 8.0E−06 0.049 0.015 Anti-BloodGroup Lewis A 0.784 0.441 0.276 2.2E−05 1.1E−03 0.224 Anti-Des-CarboxyProthrombin 0.196 0.535 0.552 2.9E−05 0.181 0.021 Anti-IgG Fc 0.9970.598 0.607 0.121 0.734 0.397 Anti-Hemoglobin 0.289 0.319 0.950 0.3120.572 0.829 Anti-Transferrin 0.704 0.321 0.151 8.9E−03 0.970 0.055

For example, anti-haptoglobin B-subunit was significantly elevated incancer relative to healthy when detected with AAL (p=3.7 E-5) butstatistically equivalent between cancer and healthy when detected withWGA (p=0.99). In contrast, anti-DCP was significantly elevated in cancerwhen detected with WGA (p=2.9 E-5) but not when detected with AAL(p=0.20). Correlations between the AAL measurements and the WGAmeasurements for each antibody were very weak, ranging from −0.05 to0.3. Clusters of the data (FIGS. 16 and 17) also revealed littleagreement between the AAL and WGA profiles. However, the clusters alsoshowed that groups of antibodies had similar profiles within each set.That observation may indication that many AAL-specific structures aresimilarly regulated and many WGA-specific structures are similarlyregulated, for the proteins measured here, but the two lectins aremeasuring glycans with different regulation. As to FIGS. 16 and 17, thesamples are ordered on the vertical axis, and the antibodies on thehorizontal axis. The left cluster shows the cancer and healthy classes(red and green labels respectively), and the right cluster shows thecancer and benign classes (red and blue labels, respectively). Redsquares indicate high binding, and green is low.

An important question in measuring changes in glycans is whether theunderlying protein concentration is changing or whether the amount ofglycan per protein is changing. The inventor investigated this questionusing parallel sandwich and lectin-detection assays to look at variationin protein and glycan levels, respectively. Serum samples (23 fromcancer and 23 from control patients) were incubated on replicate sets ofarrays, and each set of arrays was detected either with a detectionantibody to measure protein levels or with a lectin (AAL or WGA) tomeasure glycan levels (FIG. 15B). The inventor also detected a set ofarrays with the glycan-binding antibody anti-CA 19-9 (FIG. 15B). Thespecificities and potential cross-reactivities between detectionantibodies for carcinoembryonic antigen (CEA) and MUC1 were checked(FIG. 15B, left panels), and the detection antibodies were pooled toprovide multiplexed detection of both proteins. Representative images ofthe arrays showed that the proteins were present in both cancer andcontrol sera (FIG. 15A, middle panels), but detection with theanti-CA19-9 antibody showed an elevation in cancer (FIG. 15B, rightpanels). One set of 46 arrays was detected with antibodies against CEAand MUC1 to obtain the variation in protein levels of these twoproteins, and other sets of arrays were detected with the lectin AAL,the lectin WGA, or the glycan-binding antibody anti-CA 19-9. In FIG.15B, the distributions of the levels of the control samples (white bars)and the cancer samples (dark bars) are shown, in which the boxes givethe upper and lower quartiles of the measurements with respect to themedian value (horizontal line in each box), and the lines give theranges of the measurements, excluding outliers, which are represented bycircles. The number of outliers not shown is given in the upper rightcorner of each plot (FIG. 15B). The asterisks indicates the measurementsthat were statistically different (p<0.05) between the two classes. Theright three panels show the ratios of glycan levels to total proteinlevels for each of the three glycans measured.

Comparisons of the distributions of protein levels, glycan levels, andthe ratio of glycan-to-protein indicated which levels were altered incancer (FIG. 15B). CEA protein levels were increased in cancer, and theWGA-reactive and CA 19-9-reactive glycans on CEA also showed an increasein cancer. MUC1 protein levels did not change between cancer andcontrol, but all three glycans showed elevations in cancer. For both CEAand MUC1, the ratios of CA19-9 levels to total protein levels wereincreased in cancer relative to control (FIG. 15B, right panels),meaning that the carbohydrate structure targeted by the anti-CA19-9antibody was present at a higher level, per molecule, on these moleculesin the cancer sera. A comparative view of the MUC1 and CEA proteinlevels and CA 19-9 detection levels for each sample also showed theelevation in the CA 19-9-reactive structure on those molecules (FIG.15C).

As to FIG. 15A, the spots appearing at the lower right of each arraywere biotinylated control proteins. The other spots that showed signalswere anti-haptoglobin in the array detected with anti-MUC1 (second imagefrom left) and anti-CA 19-9, anti-pan CEACAM, and the WGA lectin in thearray detected with anti-CA19-9 (rightmost image).

Example 14 Microarray Fabrication and Use (for Examples 11-13)

A piezoelectric non-contact printer (Biochip Arrayer, PerkinElmer LifeSciences) spotted approximately 350 pl of each antibody solution on thesurfaces of ultra-thin-nitrocellulose-coated microscope slides (PATHslides, GenTel Biosurfaces). Forty-eight identical arrays were printedon each slide, with each array consisting of 36-48 antibodies andcontrol proteins spotted in triplicate. A wax border was imprintedaround each of the arrays to define hydrophobic boundaries, using acustom-built device.

For the experiments not using chemical blocking of the antibodies, theslides were rinsed briefly in PBST0.5, blocked for one hour at roomtemperature in PBST0.5 containing 1% BSA and 0.3% CHAPS, rinsed twicemore with PBST0.5, and dried by centrifugation prior to sampleapplication. For the experiments using chemical blocking, the blockingprocedure was performed as follows. The slides were incubated in acoupling buffer (1M sodium acetate, pH 5.5, with 0.1% Tween-20) for 30minutes, and 50 mM NaIO₄ was applied to the slides at 4° C. for 30minutes in the dark to oxidize the sugar groups. The slides were rinsedin coupling buffer and incubated with 15 mM glycerol in coupling bufferfor 5 minutes. One mM 4(4-N-maleimidophenyl)butyric acid hydrazidehydrochloride (MPBH) was incubated on the slides for two hours at roomtemperature to derivatize the carbonyl groups. The slides were rinsedbriefly with 1× phosphate-buffered saline with 0.1% Tween-20 (PBST0.1),incubated with 1 mM Cystein-Glycein dipeptide in PBST0.1 overnight at 4°C., then rinsed thoroughly and dried.

For the experiments using labeled serum proteins, the labeling procedurefollowed previously-established protocols (Haab, B. B., Dunham, M. J. &Brown, P. O. Protein microarrays for highly parallel detection andquantitation of specific proteins and antibodies in complex solutions.Genome Biology 2, 1-13 (2001); Haab, B. B. & Zhou, H. Multiplexedprotein analysis using spotted antibody microarrays. Methods Mol Biol264, 33-45 (2004)). An aliquot of each sample was reacted withNHS-digoxigenin (Molecular Probes), the unincorporated tag was removedby gel filtration (Bio-spin P6 column, Bio-Rad), and the labeled samplewas brought to a final 1:20 dilution in sample buffer (PTBST0.1containing 100 μg/ml mouse, goat, and sheep IgG, 100 μg/ml chicken IgY,200 μg/ml rabbit IgG, 0.08% Tween-20, 0.08% Brij-35 and proteaseinhibitors (Roche Biosciences, one tablet per 5 ml buffer). Forexperiments using unlabeled sera, an aliquot of each serum sample wasbrought to a final 1:20 dilution in the above sample buffer. Each serumsample was incubated on a microarray with gentle rocking at roomtemperature for one hour, and the slides were rinsed once and washedthree times for three minutes each in PBST0.1. Fluorescence emission wasdetected at 570 nm and 670 nm using a microarray scanner (ScanArrayExpress HT, PerkinElmer Life Sciences). All arrays within an experimentset were scanned in one sitting at a single laser power and detectorgain setting. The software program GenePix Pro 5.0 (Axon Instruments)was used to quantify the image data. Median local backgrounds weresubtracted from the median intensity of each spot, and data fromreplicate spots were averaged (geometric mean). The data were notnormalized.

Example 15 Serum Samples (for Examples 11-13)

Serum samples were assembled from two sites. At Evanston NorthwesternHealthcare (ENH, Evanston, Ill.), serum samples were collected frompatients with pancreatic adenocarcinoma (stage I to stage IV) or benigngastro-intestinal diseases (such as ampullary adenoma, pancreatitis,cystadenoma, or pseudocyst). Control samples also were collected at ENHfrom high-risk individuals from pancreatic cancer prone familiesundergoing surveillance with Endoscopic Ultrasound (EUS) or EndoscopicRetrograde Cholangiopancreatography (ERCP). The control subjects had nopancreatic lesions, and serum samples were collected using the sameprocedure as for the other patients at ENH. At the MultidisciplinaryPancreatic Tumor Clinic at the University of Michigan (UM) ComprehensiveCancer Center, serum samples were obtained from patients with aconfirmed diagnosis of pancreatic adenocarcinoma. Sera also wereobtained from patients with chronic pancreatitis who were seen in theGastroenterology Clinic at University of Michigan Medical Center andfrom control healthy individuals collected at University of Michiganunder the auspices of the Early Detection Research Network (EDRN). Thesamples were stored at each site at −80° C. and were sent frozen on dryice to the VARI, where they were stored at −80° C. Each aliquot had beenthawed no more than two times prior to use. All samples were coded toprotect patient anonymity and were collected under protocols approved bylocal Institutional Review Boards for human subjects research.

Example 16 Materials and Methods for Examples 17-19

Serum and Plasma Samples.

Serum samples from Evanston Northwestern Healthcare and the Universityof Michigan Medical School and plasma samples from the University ofPittsburgh School of Medicine were collected from pancreatic cancer,pancreatitis and healthy subjects (Table 1). Early-stage cancer wasdefined as stages I and II, and late-stage cancer was defined as stagesIII and IV. The pancreatitis patients were a mixture of chronic andacute. Of the 49 pancreatitis patients from the University ofPittsburgh, 39 were chronic, 4 acute, and 6 both; of the 39 pancreatitispatients from Evanston Northwestern Healthcare, 15 were acute, 11chronic, and 13 unclassified; and all of the 24 pancreatitis patientsfrom the University of Michigan were chronic. The control subjects werehealthy with no evidence of pancreatic, biliary or liver disease. Allsamples were stored at −80° C. and sent frozen on dry ice. Each aliquothad been thawed no more than three times before use.

Antibodies and Lectins.

The antibodies and lectins were obtained from various sources (see Table12). All antibodies were screened for reactivity and integrity usingWestern blots, purified, and prepared at 0.5 mg/ml in pH 7.2phosphate-buffered saline (PBS) for non-contact array printer and at0.25 mg/ml in pH 7.2 PBS for contact array printer. The steps ofantibody purification included ultracentrifugation at 47,000 g at 4degrees for 1 hour and dialysis (Slide-A-Lyzer Mini Dialysis Units,Pierce Biotechnology) against pH 7.2 PBS at 4 degree for 2 hours.

Microarray Fabrication.

Approximately 170 pg (350 pl at 500 μg/ml or 700 pl at 250 μg/ml) ofeach antibody was spotted on the surfaces of ultra-thinnitrocellulose-coated microscope slides (PATH slides, GenTelBiosciences) by a piezoelectric non-contact printer (Biochip Arrayer,PerkinElmer Life Sciences) for the slides used in ENH and UM sets, andby a non-contact microarrayer (sciFLEXARRAYER, Scienion) performed atGenTel Biosciences (Madison, Wis.) for the slides used in UP set.Forty-eight identical arrays containing triplicates of all antibodieswere printed on each slide. The triplicate spots were adjacent in thearrays used in the ENH and UM sets and were randomly dispersed in thearrays used in the UP set. Hydrophobic borders were imprinted aroundeach array using a stamping device (SlideImprinter, The Gel Company, SanFrancisco, Calif.).

Microarray Assays.

Microarray sandwich assays were performed to measure either the level oftotal CA19-9 or the glycan levels on the proteins captured by theimmobilized antibodies (FIG. 19A). The sandwich assay consisted of four1-hour-incubations in room temperature (RT) with the followingreagents: 1) blocking buffer (PBS containing 0.5% Tween-20 (PBST0.5) and1% BSA); 2) a serum or plasma sample, diluted two-fold in 1×TBScontaining 0.08% Brij, 0.08 Tween-20, 50 μg/ml protease inhibitorcocktail (Complete Protease Inhibitor Tablet, Roche Applied Science),and a cocktail of IgG from mouse, goat, and sheep each at 100 μg/ml andrabbit IgG at 200 μg/ml (Jackson ImmunoResearch Laboratories, Inc.); 3)biotinylated detection antibody or lectin (2 μg/ml), diluted in PBST0.1containing 0.1% BSA; 4) streptavidin-phycoerythrin (10 μg/ml, RocheApplied Science), diluted in PBST0.1 containing 0.1% BSA. After eachstep, the slides were rinsed in three baths of PBST0.1 and dried bycentrifugation (Eppendorf 5810R, rotor A-4-62, 1500×g).

The measurement of glycans by using lectins detection on the capturedproteins (FIG. 19A) was carried out as above, except the glycans on thespotted antibodies were derivatized to prevent lectin binding to theantibodies [10], and the arrays were probed with glycan-binding lectins.

Fluorescence emission from the phycoerythrin was detected at 570 nmusing a microarray scanner (LS Reloaded, Tecan). All arrays within oneslide were scanned at a single laser power and detector gain setting.The images were quantified using the software program GenePix Pro 5.0(Molecular Devices, Sunnyvale, Calif.). Spots were identified usingautomated spot-finding or manual adjustments for occasionalirregularities. The median local backgrounds were subtracted from themedian intensity of each spot, and triplicate spots were averaged usingthe geometric mean. The coefficient of variation between replicateanalyzed spots was typically under 10%.

Statistical Analyses and Software.

Pearson correlations, Student's T-tests, and receiver-operatorcharacteristic analyses were calculated using Microsoft Excel. Thescatter and box plots were created using OriginPro 8, and figureproduction was performed using Canvas X. Clustering and visualizationwere performed using the programs Cluster and Treeview andMultiExperiment Viewer.

Example 17 Profiling the Disease Specificity of the CA 19-9 Antigen onSpecific Proteins

Antibody arrays were generated to target CA 19-9 and the mucin proteinsMUC1, MUC5AC, and MUC16. Table 12 shows the antibodies used on thearrays for detection.

TABLE 12 ID # Name (clone) Source Cat. No 341 anti-CA19-9 (1B.844)Usbiological C0075-07 340 anti-MUC1 (CA15-3) Usbiological C0050-23 684anti-MUC1 (SM3) Abcam ab22711 1093 anti-MUC1 (CM1) GeneTex GTX10114 1094anti-HMFG (2Q437) USBiological M3897-08 1095 anti-MUC1 (695) AcrisBM3359 829 anti-MUC5ac (CLH2) Chemicon MAB2011 831 anti-MUC5ac (45M1)Biogenesis 1695-0128 1091 anti-MUC5ac (1-13M1) Thermo Scientific MS-551-PABX 1092 anti-MUC5ac (2-12M1) AbD SeroTec 0200-0557 1251 anti-MUC5ac(2-11M1) ABR MA1-35704 339 anti-MUC16 (CA125) Usbiological C0050-01 830anti-MUC16 (x325) Abcam ab10033 1098 anti-MUC16 (x306) Novus BiologicalsNB120-10032 1099 anti-MUC16 (1.B.826_C USBiological C0050-05 domain)1270 anti-Apolipoprotein B Abcam ab39560 (7B8) 517 Bauhinia purpurealectin Vector Labs B-1285 (BPL)

The mucins MUC1, MUC5AC, and MUC16 were targeted based on results from aprevious study that showed increased levels and altered glycosylation ofthese proteins in the blood of pancreatic cancer patients [11]. Four tofive different monoclonal antibodies were used for each protein, andeach antibody was printed in triplicate. The locations of the triplicatespots were randomized to minimize potential positional bias within eacharray. Serum and plasma samples were incubated on the arrays, and thearrays were probed with the CA 19-9 antibody to detect either the totallevel of its target antigen (detected at the CA 19-9 capture antibody)or its level on particular proteins (detected at the capture antibodiesagainst specific proteins) (FIG. 19A). The ability to print and process48 antibody arrays on a single microscopic slide enabled the efficientevaluation of multiple clinical samples (FIG. 19A). Dilution curves ofpooled serum/plasma samples generated in our previous study [11]confirmed the detection of the targeted proteins or glycans in thelinear response range at a 1:2 dilution, and the use of negative controlantibodies (mouse mAbs lacking specificity for any human protein) andnegative control arrays (arrays incubated with PBS buffer instead ofserum or plasma) confirmed a lack of non-specific binding to the captureantibodies by the detection reagents. The various capture antibodiesdisplayed distinct binding patterns (FIG. 19B), consistent with theunique specificities of the antibodies.

As shown in Table 13, three independent sample sets of serum and plasma,obtained from three different institutions, were processed.

TABLE 13 Late- stage Early-stage cancer Set cancer (Stage (Stage Pancre-# Set provider I, II) III, IV) atitis Healthy Total 1 Evanston 33 2026/39 20 Northwestern Healthcare (ENH) 2 University of 40 24 24 420Michigan (UM) 3 University of 58 58 49 43 Pittsburgh (UP)

Sample set #3 was processed blinded and in duplicate on different dayswith distinct batches of microarrays. As in initial comparison of totalCA 19-9 to CA 19-9 on individual proteins, we characterized theperformance of the individual measurements for discriminating betweenpancreatic cancer and pancreatitis. In each set, the total CA 19-9 levelwas significantly higher in the cancer patients than in the pancreatitispatients (p<0.001, student's t-test). The sensitivity of detectingcancer was 79-90% at a specificity of 75% in the three sets (FIG. 20),which included discrimination of both early-stage (stages I and II) andlate-stage (stages III and IV) pancreatic cancer patients frompancreatitis patients. The performance of CA 19-9 detection on theindividual mucins was similar to or slightly better than total CA 19-9for certain antibodies in each set. Table 14 shows the performance ofindividual markers for cancer from pancreatitis in each sample set. Thearea-under-the-curve (AUC) in receiver-operator characteristic analysisis given.

TABLE 14 Set 1, Set 1, Anti- Early- Late- Set Set 3, Ave- Marker bodyStage Stage Set 2 3 Repeat rage Total 341 0.81 0.81 0.91 0.87 0.88 0.86CA19-9 CA19-9 on 1093 0.82 0.93 0.89 0.81 0.8 0.85 MUC1 340 0.65 0.860.71 0.66 0.72 684 0.52 0.8 0.66 1094 0.52 0.84 0.68 1095 0.68 0.63 0.66CA19-9 on 831 0.77 0.92 0.75 0.81 0.8 0.81 MUC5ac 1091 0.8 0.86 0.910.63 0.52 0.74 1092 0.55 0.87 0.71 1251 0.79 0.78 0.79 CA19-9 on 3390.58 0.8 0.69 MUC16 830 0.62 0.82 0.9 0.86 0.85 0.81 1098 0.66 0.8 0.90.82 0.78 0.79 1099 0.51 0.81 0.83 0.81 0.78 0.75

Although no individual marker showed a consistent, significantimprovement over total CA 19-9, the fact that similar discriminationcould be achieved between total CA 19-9 and CA 19-9 on individualcarriers indicates that these mucin proteins are majordisease-associated carriers of the CA 19-9 antigen.

Example 18 Patient Subgroups Based on CA 19-9 Carrier Proteins

The inventors next investigated the relationships between the carrierproteins of CA 19-9, to determine whether complementarity between themeasurements might yield improved performance if used in combination.The inventors specifically investigated whether subgroups of patientsexist that are defined by the proteins that carry CA 19-9. If particularmarkers are specific for distinct, non-overlapping subgroups, then themarkers could be used in combination for improved overall performance.This potential was supported by the lack of significant correlationbetween total CA 19-9 and CA 19-9 on individual proteins or between theindividual proteins.

The primary images from selected samples provide insights into therelationships between the carrier proteins (FIG. 21). The amount ofsignal at the various capture antibodies indicates where the CA 19-9antigen is found. In samples with elevated CA 19-9, most of the mucinproteins captured here display CA 19-9. Among the samples with total CA19-9 levels below the 75% specificity threshold, about half show that atleast one of the mucins captured here display the CA 19-9 antigen (theprominent mucin carriers are indicated). Other samples show discernabletotal CA 19-9 but show that these mucins are not carriers of theantigen, and a smaller subset shows no detectable total CA 19-9. Acomprehensive and more quantitative view of the relationships betweenthe carrier proteins can be obtained using clusters (FIGS. 22A-C). Asnoted above, most patients with highly-elevated total CA 19-9 (in thetop quintile) display the antigen on all three mucins, although certainpatients do not display it on one or all of the mucins (FIG. 22A). Asimilar relationship holds for patients mid-range (FIG. 22B) and low(FIG. 22C) total CA 19-9 values, with a greater percentage showingdiversity in the mucins that carry CA 19-9 or that show no detection ofCA 19-9 on mucins. Some of the pancreatitis patients also had mucins ascarriers of the CA 19-9 antigen, indicating that the mucins are notpurely cancer-specific CA 19-9 carriers. Similar subgroups were found inSets 1 and 2 (not shown), and Western blot analysis confirmed thesepatterns of CA 19-9 distribution in selected plasma samples (FIG. 25).These findings support the concepts that mucins are major carriers ofthe CA 19-9 antigen even in low total CA 19-9 states; that diversityexists between people in which mucins carry the antigen; and that otherprotein besides the mucins probed here carry the CA 19-9 antigen in somepatients.

The above observations explain why the detection of CA 19-9 on any ofthese individual proteins does not out-perform total CA 19-9. However,this subgrouping of patients may guide the development of complementarymarker panels. First, patients with highly-elevated total CA 19-9 arevery likely to have malignancy based on that measurement alone, andadditional markers may not be necessary. Second, among patients withmoderate total CA 19-9 levels, the detection of the CA 19-9 antigen onthe protein that is the predominant carrier may provide selectivediscrimination from benign disease. For some patients, the predominantCA 19-9 carrier is a mucin, but for other patients, other carrierproteins should be sought. Third, for the patients with undetectabletotal CA 19-9, other glycans on mucins or other proteins are requiredfor detection.

The possibility of detecting other glycans to complement the CA 19-9antigen was suggested by the primary images (FIG. 21). The set of 177samples had been run with detection using the Bauhinea Purpurea lectin(BPL) and Wheat Germ Agglutinin (WGA) as a preliminary look at otherglycans besides the CA 19-9 antigen. One of the cancer samples thatshowed negligible signal at any antibody using CA 19-9 detection (sampleLC3607) showed clear signal at the MUC5AC antibody using detection withBPL. This result indicates that the MUC5AC mucin is present in thesample and that it does not carry the CA 19-9 antigen, but that it maybe detected using another glycan. This comparison suggests theimportance of detecting other glycans besides the CA19-9 antigen forfurther performance improvement, especially in the cancer patients withno CA19-9 present.

Example 19 Improved Accuracy of Cancer Detection Using CA 19-9 onIndividual Proteins

The above observations compelled the investigation of whether CA 19-9 onindividual proteins could complement total CA 19-9 measurements forimproved biomarker performance. The relationship between themeasurements of total CA 19-9 and CA 19-9 on certain individual proteinsshowed this possibility (FIGS. 23A-C). In some cases, patients that werelow in total CA 19-9 were distinguishable from pancreatitis patients bytheir CA19-9 level on MUC16 (FIG. 23A). A threshold could be set forthis marker by which five cancer patients but no pancreatitis patientswere elevated. In searching the entire UP sample set of 177 pancreaticcancer and pancreatitis samples, 11 markers each picked up at least oneof the samples not detected by total CA19-9 (FIG. 26). To simplify thepanel to the minimum number of markers necessary to pick up maximumadditional samples, a reduced set of four markers, CA 19-9 detection onMUC1 (#1095), MUC5ac (#1251), and MUC16 (#830), and BPL detection onMUC5ac (#1251), was identified (FIG. 23B). Among the 124 cancer samplesin this set, 109 were detectable by total CA 19-9 (88% sensitivity at75% specificity). Using a combination rule in which an elevation(defined by the threshold determined individually for each marker) inany member of the panel indicates a “case,” and a lack of elevation inall markers indicates a “control,” the marker panel picked up eight ofthe initial 15 false negative cancer samples, achieving 94% sensitivityand 75% specificity (FIG. 23B). This biomarker panel preferentiallypicked up the late-stage patients, which reflected the relativeelevation of some of the individual markers in late-stage patients(Table 14).

This strategy and the resulting biomarker panel were consistent in therepeat dataset of the 177 samples. When the inventors applied the panelselection strategy described above to the repeat dataset, the samemarkers were selected, and each additional marker picked up the samefalse negative samples in both sets (not shown). In addition, thethresholds for each marker determined from one set could be applied tothe other set to achieve the same improvement in sensitivity and to pickup the same additional samples (not shown). This reproducibility inmarker selection was confirmed by the strong correlation between therepeat datasets for all CA 19-9 measurements (r>0.8, Pearson's rcorrelation coefficient). These results confirm the reproducibility ofthe marker panel selected for this sample set and the reliability of thepanel selection strategy.

The inventors next asked whether consistent results could be achieved inthe other two datasets. The MUC16 (#830) antibody was used in alldatasets and was the most important to validate since it picked up themost cancer patients. In the late-stage cancer sample sets, CA19-9 onMUC16 picked up one of the four false negatives from Set 2 and two ofthe three false negatives from Set 1 (FIG. 24A). No early-stage patientswere picked up from Set 1, but it is clear that many patients haveelevations in this marker at higher thresholds, indicating that it ispresent even in early-stage cancer. This consistent improvement in threeindependent sample sets provided initial validation that CA 19-9 onMUC16 (using the #830 antibody) can complement total CA 19-9 forimproved sensitivity.

MUC1 and MUC5ac were targeted in Sets 1 and 2 but with antibodies notincluded in the biomarker panel from Set 3. Therefore the inventorstested the consistency of the protein markers independent of particularantibodies. Of the 17 late-stage cancer patients in Set 1, thefalse-negative sample was picked up by CA19-9 on MUC1 (#1093) (twosamples were inconclusive due to missing antibody array data due tofailed experiments), and of the 33 early-stage cancer patients in Set 1,two of six also were picked up by CA 19-9 on MUC1 (#1093) (FIG. 24B). CA19-9 on MUC1 (#1093) also picked up one of the four false negativepatients in Set 2 (FIG. 24C). CA19-9 on MUC5ac (#831) produced the samecomplementarity as CA19-9 on MUC16 in the late-stage patients of Set 1(FIG. 24B). Therefore, both MUC1 and MUC5AC contributed to the detectionof patients with low total CA 19-9 using antibodies not used in Set 3.Importantly, no other antibodies on the arrays produced complementarydetection to total CA 19-9 (data not shown), confirming that the effectis specific to these proteins. Between these sets, 25-100% of the falsepositive samples as defined by total CA 19-9 were detected using CA 19-9on individual proteins, which is consistent with the result of 7/15(47%) picked up in Set 3. This result provides initial validation thatthe sensitivity of pancreatic cancer detection can be improved using amarker panel of CA 19-9 measurements on MUC16, MUC1, and MUC5AC, usingselected antibodies. This improvement may also be possible forearly-stage cancer (FIG. 24B).

Table 15, summarizes the performance of discriminating pancreatic cancerfrom pancreatitis for total CA19-9 and panels of CA 19-9 measurements onindividual proteins.

TABLE 15 % False negatives picked up by Specificity Sensitivity Accuracypanel Total CA19-9   75% 87.9% 84.3%   47% in Set 3 #1 (36/48) (109/124) (7 of 15) Panel in Set 3   75% 93.5% 88.3% #1 (36/48) (116/124) TotalCA19-9 75.5% 84.4% 81.9% 36.8% in Set 3 #2 (37/49) (109/128)  (7 of 19)Panel in Set 3 75.5% 90.6% 86.4% #2 (37/49) (116/128) Total CA19-9 76.9%78.8% 78.0% 28.6% in Set 1 - Early- (20/26) (26/33) (2 of 7) Stage Panelin Set 1 - 76.9% 84.8% 81.4% Early-Stage (20/26) (28/33) Total CA19-976.9% 84.2% 79.3%  100% in Set 1 - Late- (30/39) (16/19) (3 of 3) StagePanel in Set 1 - 76.9%  100% 84.5% Late-Stage (30/39) (19/19) TotalCA19-9   75%   90% 84.4%   25% in Set 2 (18/24) (36/40) (1 of 4) Panelin Set 2   75% 92.5% 86.0% (18/24) (37/40)

These results comprise 209 samples from cancer patients (100 early-stageand 109 late-stage) and 112 samples from pancreatitis patients,collected at three different institutions and analyzed in fiveindependent experiment sets. At a fixed specificity of approximately75%, an improvement in sensitivity was observed in all experiment sets.This improvement led to an elevation of the sensitivity of cancerdetection from 78-84% by total CA19-9 to 81-100% by the panel.

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What is claimed is:
 1. A method for differentiating pancreatic cancerfrom a benign pancreatic disease, comprising the steps: obtaining apatient biological sample from a patient having or suspected of having apancreatic disease; assaying the patient biological sample (a) to detecta total level of CA 19-9 antigen in the patient biological sample and(b) to detect a glycan level in MUC16 in the patient biological sample;comparing the total level of CA 19-9 antigen in the patient biologicalsample to a statistically validated threshold for total CA 19-9 antigen,which statistically validated threshold for total CA 19-9 antigen isbased on a total level of CA 19-9 antigen in comparable controlbiological samples from patients having a benign pancreatic disease; andcomparing the glycan level in the MUC16 in the patient biological sampleto a statistically validated threshold for the MUC16, whichstatistically validated threshold for the MUC16 is based on a glycanlevel in the MUC16 in comparable control biological samples frompatients having a benign pancreatic disease; wherein (a) a differentlevel of total CA 19-9 antigen in the patient biological sample ascompared to the statistically validated threshold for total CA 19-9antigen and (b) a different level of glycan level in the MUC16 in thepatient biological sample as compared to the statistically validatedthreshold for the MUC16 indicate that the patient has pancreatic cancerrather than a benign pancreatic disease.
 2. The method of claim 1,wherein pancreatitis is the benign pancreatic disease.
 3. The method ofclaim 1, further comprising the step of diagnosing, pancreatic cancer inthe patient.
 4. The method of claim 1, further comprising reporting theindication of pancreatic cancer to the patient or a physician.
 5. Themethod of claim 1, further comprising providing a treatment forpancreatic cancer to the patient.
 6. The method of claim 1, furthercomprising providing a monoclonal antibody to the CA-19-9 antigen andusing the monoclonal antibody in assaying for both (a) the total CA 19-9antigen in the patient biological sample and (b) the glycan level in theMUC16 in the patient biological sample.
 7. The method of claim 1,further comprising the steps of: assaying the patient biological sampleto detect a glycan level in MUC1 in the patient biological sample;comparing the glycan level in the MUC1 in the patient biological sampleto a statistically validated threshold for the MUC1, which statisticallyvalidated threshold for the MUC1 is based on a glycan level in the MUC1in comparable control biological samples from patients having a benignpancreatic disease; wherein (a) a different level of total CA 19-9antigen in the patient biological sample as compared to thestatistically validated threshold for total CA 19-9 antigen, (b) adifferent level of glycan level in the MUC16 in the patient biologicalsample as compared to the statistically validated threshold for theMUC16, and (c) a different level of glycan level in the MUC1 in thepatient biological sample as compared to the statistically validatedthreshold for the MUC1 indicate that the patient has pancreatic cancerrather than a benign pancreatic disease.
 8. The method of claim 7,further comprising providing a glycan binding protein other than amonoclonal antibody to the CA 19-9 antigen and using the glycan bindingprotein in assaying MUC16.
 9. The method of claim 8, wherein the glycanbinding protein other than the monoclonal antibody to the CA 19-9antigen is Bauhinea Purpurea lectin (BPL).
 10. The method of claim 1,wherein the patient biological sample is plasma or serum from thepatient.